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Statistics For Data Science | Statistics Using R Programming Language | Hypothesis Testing | Edureka

The document provides an introduction to statistics, covering key terminology, categories (descriptive and inferential statistics), and their applications in R. It includes explanations of basic statistical terms such as population, sample, and variable, as well as methods of analysis and measures of central tendency and spread. It also highlights the use cases for descriptive and inferential statistics with examples and calculations.

Introduction to Statistics Terminology Categories in Statistics Descriptive & Inferential Statistics Statistics in R Descriptive Statistics in R Inferential Statistics in R Agenda
Introduction to Statistics Terminology Categories in Statistics Descriptive & Inferential Statistics Statistics in R Descriptive Statistics in R Inferential Statistics in R Agenda
Introduction to Statistics Terminology Categories in Statistics Descriptive & Inferential Statistics Statistics in R Descriptive Statistics in R Inferential Statistics in R Agenda
Introduction to Statistics Terminology Categories in Statistics Descriptive & Inferential Statistics Statistics in R Descriptive Statistics in R Inferential Statistics in R Agenda
Introduction to Statistics Terminology Categories in Statistics Descriptive & Inferential Statistics Statistics in R Descriptive Statistics in R Inferential Statistics in R Agenda
Introduction to Statistics Terminology Categories in Statistics Descriptive & Inferential Statistics Statistics in R Descriptive Statistics in R Inferential Statistics in R Agenda
Introduction to Statistics Terminology Categories in Statistics Descriptive & Inferential Statistics Statistics in R Descriptive Statistics in R Inferential Statistics in R Agenda
Introduction to Statistics Terminology Categories in Statistics Descriptive & Inferential Statistics Statistics in R Descriptive Statistics in R Inferential Statistics in R Agenda
Introduction to Statistics
Copyright © 2018, edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Introduction to Statistics Statistics is a branch of mathematics dealing with data collection and organization, analysis, interpretation and presentation.
Copyright © 2018, edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Introduction to Statistics Statistics is a branch of mathematics dealing with data collection and organization, analysis, interpretation and presentation.
Copyright © 2018, edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Introduction to Statistics Statistics is a branch of mathematics dealing with data collection and organization, analysis, interpretation and presentation. Analyse Data Build a Model Infer Result
Copyright © 2018, edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Introduction to Statistics Statistics is a branch of mathematics dealing with data collection and organization, analysis, interpretation and presentation. Statistics Stock Market Life Sciences Weather Retail Insurance Education
Terminology
Copyright © 2018, edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Basic Terminology There are a few statistical terms one should be aware of while dealing with statistics. Population ParameterSample Variable
Copyright © 2018, edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Basic Terminology There are a few statistical terms one should be aware of while dealing with statistics. Population ParameterSample Variable Population is the set of sources from which data has to be collected.
Copyright © 2018, edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Basic Terminology There are a few statistical terms one should be aware of while dealing with statistics. Population ParameterSample Variable A Sample is a subset of the Population.
Copyright © 2018, edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Basic Terminology There are a few statistical terms one should be aware of while dealing with statistics. Population ParameterSample Variable A variable is any characteristics, number, or quantity that can be measured or counted. A variable may also be called a data item. Gender Age Region Height Weight Income Blood Group Ethnicity Degree Time Language
Copyright © 2018, edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Basic Terminology There are a few statistical terms one should be aware of while dealing with statistics. Population ParameterSample Variable Also known as a statistical model, A statistical Parameter or population parameter is a quantity that indexes a family of probability distributions. µ ∑ х
Copyright © 2018, edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Types of Analysis An analysis can be done in one of two ways. Analysis Quantitative Qualitative
Copyright © 2018, edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Types of Analysis An analysis can be done in one of two ways. Also known as Statistical Analysis, it is the science of collecting & interpreting objects with numbers. Also known as Non-statistical Analysis, it mostly deals with generic data using text, media, etc Analysis Quantitative Qualitative
Categories in Statistics
Copyright © 2018, edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Inferential statistics makes inferences and predictions about a population based on a sample of data taken from the population in question. Descriptive statistics uses the data to provide descriptions of the population, either through numerical calculations or graphs or tables. Categories in Statistics There are two major categories in Statistics. Descriptive InferentialInferential
Copyright © 2018, edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Descriptive Statistics This method, is mainly focused upon the main characteristics of data. It provides graphical summary of the data. Characteristics of Data Descriptive Statistics
Copyright © 2018, edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Descriptive Statistics Maximum Minimum Average This method, is mainly focused upon the main characteristics of data. It provides graphical summary of the data.
Copyright © 2018, edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Inferential Statistics This method, generalizes a large dataset and applies probability to draw a conclusion. It allows us to infer data parameters based on a statistical model using a sample data. Statistical Model Start Process Step Decision Answer Choice I Choice II Inferential Statistics
Copyright © 2018, edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Inferential Statistics Tall Short Average This method, generalizes a large dataset and applies probability to draw a conclusion. It allows us to infer data parameters based on a statistical model using a sample data.
Descriptive Statistics – Statistical Measures
Copyright © 2018, edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Descriptive Statistics – Use Case Here is a sample dataset of cars containing the variables: Cars, Mileage per Gallon(mpg), Cylinder Type (cyl), Displacement (disp), Horse Power(hp) & Real Axle Ratio(drat). Using descriptive Analysis, you can analyse each of the variables in the dataset for mean, standard deviation, minimum and maximum. Cars mpg cyl disp hp drat A 21 6 160 110 3.9 B 21 6 160 110 3.9 C 22.8 4 108 93 3.85 D 21.3 6 108 96 3 E 23 4 150 90 4 F 23 6 108 110 3.9 G 23 4 160 110 3.9 H 23 6 160 110 3.9
Copyright © 2018, edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Measures of the Centre There are a few statistical terms one should be aware of while dealing with statistics. Mean Median Mode
Copyright © 2018, edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Descriptive Statistics – Use Case If we want to find out the average horsepower of the cars among the population of cars, we will check and calculate the average of all values. In this case, Cars mpg cyl disp hp drat A 21 6 160 110 3.9 B 21 6 160 110 3.9 C 22.8 4 108 93 3.85 D 21.3 6 108 96 3 E 23 4 150 90 4 F 23 6 108 110 3.9 G 23 4 160 110 3.9 H 23 6 160 110 3.9 110 + 110 + 93 + 96 + 90 + 110 + 110 + 110 8 = 103.625
Copyright © 2018, edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Measures of the Centre There are a few statistical terms one should be aware of while dealing with statistics. Mean Median Mode Measure of average of all the values in a sample is called Mean.
Copyright © 2018, edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Descriptive Statistics – Use Case If we want to find out the centre value of mpg among the population of cars, we will arrange the mpg values in ascending order to choose the middle value. In this case, 21,21,21.3,22.8,23,23,23,23 But in case of even entries, we take average of the two middle values. In this case, 22.8+23 2 = 22.9 Cars mpg cyl disp hp drat A 21 6 160 110 3.9 B 21 6 160 110 3.9 C 22.8 4 108 93 3.85 D 21.3 6 108 96 3 E 23 4 150 90 4 F 23 6 108 110 3.9 G 23 4 160 110 3.9 H 23 6 160 110 3.9
Copyright © 2018, edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Measures of the Centre There are a few statistical terms one should be aware of while dealing with statistics. Mean Median Mode Measure of the central value of the sample set is called Median.
Copyright © 2018, edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Descriptive Statistics – Use Case If we want to find out the most common type of cylinder among the population of cars, we will check the value which is repeated most number of times. 4 6 4 6 Cars mpg cyl disp hp drat A 21 6 160 110 3.9 B 21 6 160 110 3.9 C 22.8 4 108 93 3.85 D 21.3 6 108 96 3 E 23 4 150 90 4 F 23 6 108 110 3.9 G 23 4 160 110 3.9 H 23 6 160 110 3.9
Copyright © 2018, edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Measures of the Centre There are a few statistical terms one should be aware of while dealing with statistics. Mean Median Mode The value most recurrent in the sample set is known as Mode.
Copyright © 2018, edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Measures of the Spread There are a few statistical terms one should be aware of while dealing with statistics. Range Standard DeviationInter Quartile Range Variance
Copyright © 2018, edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Measures of Spread There are a few statistical terms one should be aware of while dealing with statistics. Range Standard DeviationInter Quartile Range Variance Range is the given measure of how spread apart the values in a dataset are. Range = Max(𝑥𝑖) - Min(𝑥𝑖)
Copyright © 2018, edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Measures of Spread There are a few statistical terms one should be aware of while dealing with statistics. Range Standard DeviationInter Quartile Range Variance Inter Quartile Range(IQR) is the measure of variability, based on dividing a dataset into quartiles. 1 2 3 4 5 6 7 8
Copyright © 2018, edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Measures of Spread There are a few statistical terms one should be aware of while dealing with statistics. Range Standard DeviationInter Quartile Range Variance Quartile 1 2 3 4 5 6 7 8 Q1 Q2 Q3
Copyright © 2018, edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Measures of Spread There are a few statistical terms one should be aware of while dealing with statistics. Range Standard DeviationInter Quartile Range Variance Quartile 1 2 3 4 5 6 7 8 Q1 Q2 Q3 Q1= 2+3 2 =2.5
Copyright © 2018, edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Measures of Spread There are a few statistical terms one should be aware of while dealing with statistics. Range Standard DeviationInter Quartile Range Variance Quartile 1 2 3 4 5 6 7 8 Q1 Q2 Q3 Q2= 4+5 2 =4.5
Copyright © 2018, edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Measures of Spread There are a few statistical terms one should be aware of while dealing with statistics. Range Standard DeviationInter Quartile Range Variance Quartile 1 2 3 4 5 6 7 8 Q1 Q2 Q3 Q3= 6+7 2 =6.5
Copyright © 2018, edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Measures of Spread There are a few statistical terms one should be aware of while dealing with statistics. Range Standard DeviationInter Quartile Range Variance Inter Quartile Range 1 2 3 4 5 6 7 8 Q1 Q3
Copyright © 2018, edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Measures of Spread There are a few statistical terms one should be aware of while dealing with statistics. Range Standard DeviationInter Quartile Range Variance Variance describes how much a random variable differs from its expected value. It entails computing squares of deviations.
Copyright © 2018, edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Measures of Spread There are a few statistical terms one should be aware of while dealing with statistics. Range Standard DeviationInter Quartile Range Variance ❖ Deviation is the difference between each element from the mean. Deviation = (𝑥𝑖-µ)
Copyright © 2018, edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Measures of Spread There are a few statistical terms one should be aware of while dealing with statistics. Range Standard DeviationInter Quartile Range Variance ❖ Population Variance is the average of squared deviations. σ² = ෍ 𝑖=1 𝑁 = (𝑥𝑖−𝜇)² 1 𝑁
Copyright © 2018, edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Measures of Spread There are a few statistical terms one should be aware of while dealing with statistics. Range Standard DeviationInter Quartile Range Variance ❖ Sample Variance is the average of squared differences from the mean. s² = ෍ 𝑖=1 𝑁 = (𝑥𝑖− ҧ𝑥)² 1 (𝑛 − 1)
Copyright © 2018, edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Measures of Spread There are a few statistical terms one should be aware of while dealing with statistics. Range Standard DeviationInter Quartile Range Variance Standard Deviation is the measure of the dispersion of a set of data from its mean. σ = ෍ 𝑖=1 𝑁 = (𝑥𝑖−𝜇)² 1 𝑁
Copyright © 2018, edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Standard Deviation– Use Case Ross has 20 Dinosaur figures. They have the numbers 9, 2, 5, 4, 12, 7, 8, 11, 9, 3, 7, 4, 12, 5, 4, 10, 9, 6, 9, 4. Work out the Standard Deviation. Find out the mean for your sample set. STEP 1 The Mean is: 9+2+5+4+12+7+8+11+9+3+7+4+12+5+4+10+9+6+9+4 20 ⸫µ=7
Copyright © 2018, edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Standard Deviation– Use Case Ross has 20 Dinosaur figures. They have the numbers 9, 2, 5, 4, 12, 7, 8, 11, 9, 3, 7, 4, 12, 5, 4, 10, 9, 6, 9, 4. Work out the Standard Deviation. Then for each number, subtract the Mean and square the result. STEP 2 (𝑥𝑖−𝜇)² (9-7)²= 2²=4 (2-7)²= (-5)²=25 (5-7)²= (-2)²=4 And so on… ⸫ We get the following results: 4, 25, 4, 9, 25, 0, 1, 16, 4, 16, 0, 9, 25, 4, 9, 9, 4, 1, 4, 9
Copyright © 2018, edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Standard Deviation– Use Case Ross has 20 Dinosaur figures. They have the numbers 9, 2, 5, 4, 12, 7, 8, 11, 9, 3, 7, 4, 12, 5, 4, 10, 9, 6, 9, 4. Work out the Standard Deviation. Then work out the mean of those squared differences. STEP 3 ෍ 𝑖=1 𝑁 = (𝑥𝑖−𝜇)² 1 𝑁 4+25+4+9+25+0+1+16+4+16+0+9+25+4+9+9+4+1+4+9 20 ⸫ σ² = 8.9
Copyright © 2018, edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Standard Deviation– Use Case Ross has 20 Dinosaur figures. They have the numbers 9, 2, 5, 4, 12, 7, 8, 11, 9, 3, 7, 4, 12, 5, 4, 10, 9, 6, 9, 4. Work out the Standard Deviation. Take square root of σ². STEP 4 ⸫ σ = 2.983 ෍ 𝑖=1 𝑁 = (𝑥𝑖−𝜇)² 1 𝑁 σ =
Statistics in R
Copyright © 2018, edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Statistics in R ❖ R is open-source and freely available. ❖ R is cross-platform compatible. ❖ R is a powerful scripting language. ❖ R is highly flexible and evolved. Reasons for moving to R
Copyright © 2018, edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Statistics in R ❖ R is open-source and freely available. ❖ R is cross-platform compatible. ❖ R is a powerful scripting language. ❖ R is highly flexible and evolved. Reasons for moving to R
Copyright © 2018, edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Statistics in R ❖ R is open-source and freely available. ❖ R is cross-platform compatible. ❖ R is a powerful scripting language. ❖ R is highly flexible and evolved. Reasons for moving to R
Copyright © 2018, edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Statistics in R ❖ R is open-source and freely available. ❖ R is cross-platform compatible. ❖ R is a powerful scripting language. ❖ R is highly flexible and evolved. Reasons for moving to R
Descriptive statistics in R
Inferential Statistics – Hypothesis Testing
Copyright © 2018, edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Hypothesis Testing Statisticians use hypothesis testing to formally check whether the hypothesis is accepted or rejected. Hypothesis testing is conducted in the following manner: ❖ State the Hypotheses – This stage involves stating the null and alternative hypotheses. ❖ Formulate an Analysis Plan – This stage involves the construction of an analysis plan. ❖ Analyse Sample Data – This stage involves the calculation and interpretation of the test statistic as described in the analysis plan. ❖ Interpret Results – This stage involves the application of the decision rule described in the analysis plan.
Copyright © 2018, edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Hypothesis Testing Nick John Bob Harry Assume the event is free of bias. So, what is the probability of John not cheating?
Copyright © 2018, edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Hypothesis Testing Nick John Bob Harry P(John not picked for a day) = 3 4 P(John not picked for 3 days) = 3 4 × 3 4 × 3 4 = 0.42 (approx) P(John not picked for 12 days) = ( 3 4 ) 12 = 0.032 < 𝟎. 𝟎𝟓
Copyright © 2018, edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Hypothesis Testing Nick John Bob Harry Null Hypothesis (𝑯 𝟎) : Result is no different from assumption. Alternate Hypothesis (𝑯 𝒂) : Result disproves the assumption. Probability of Event < 𝟎. 𝟎𝟓 (5%)
Inferential Statistics in R
www.edureka.co/masters-program/business-intelligence-certification

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Statistics For Data Science | Statistics Using R Programming Language | Hypothesis Testing | Edureka

  • 2.
    Introduction to Statistics Terminology Categoriesin Statistics Descriptive & Inferential Statistics Statistics in R Descriptive Statistics in R Inferential Statistics in R Agenda
  • 3.
    Introduction to Statistics Terminology Categoriesin Statistics Descriptive & Inferential Statistics Statistics in R Descriptive Statistics in R Inferential Statistics in R Agenda
  • 4.
    Introduction to Statistics Terminology Categoriesin Statistics Descriptive & Inferential Statistics Statistics in R Descriptive Statistics in R Inferential Statistics in R Agenda
  • 5.
    Introduction to Statistics Terminology Categoriesin Statistics Descriptive & Inferential Statistics Statistics in R Descriptive Statistics in R Inferential Statistics in R Agenda
  • 6.
    Introduction to Statistics Terminology Categoriesin Statistics Descriptive & Inferential Statistics Statistics in R Descriptive Statistics in R Inferential Statistics in R Agenda
  • 7.
    Introduction to Statistics Terminology Categoriesin Statistics Descriptive & Inferential Statistics Statistics in R Descriptive Statistics in R Inferential Statistics in R Agenda
  • 8.
    Introduction to Statistics Terminology Categoriesin Statistics Descriptive & Inferential Statistics Statistics in R Descriptive Statistics in R Inferential Statistics in R Agenda
  • 9.
    Introduction to Statistics Terminology Categoriesin Statistics Descriptive & Inferential Statistics Statistics in R Descriptive Statistics in R Inferential Statistics in R Agenda
  • 10.
  • 11.
    Copyright © 2018,edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Introduction to Statistics Statistics is a branch of mathematics dealing with data collection and organization, analysis, interpretation and presentation.
  • 12.
    Copyright © 2018,edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Introduction to Statistics Statistics is a branch of mathematics dealing with data collection and organization, analysis, interpretation and presentation.
  • 13.
    Copyright © 2018,edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Introduction to Statistics Statistics is a branch of mathematics dealing with data collection and organization, analysis, interpretation and presentation. Analyse Data Build a Model Infer Result
  • 14.
    Copyright © 2018,edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Introduction to Statistics Statistics is a branch of mathematics dealing with data collection and organization, analysis, interpretation and presentation. Statistics Stock Market Life Sciences Weather Retail Insurance Education
  • 15.
  • 16.
    Copyright © 2018,edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Basic Terminology There are a few statistical terms one should be aware of while dealing with statistics. Population ParameterSample Variable
  • 17.
    Copyright © 2018,edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Basic Terminology There are a few statistical terms one should be aware of while dealing with statistics. Population ParameterSample Variable Population is the set of sources from which data has to be collected.
  • 18.
    Copyright © 2018,edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Basic Terminology There are a few statistical terms one should be aware of while dealing with statistics. Population ParameterSample Variable A Sample is a subset of the Population.
  • 19.
    Copyright © 2018,edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Basic Terminology There are a few statistical terms one should be aware of while dealing with statistics. Population ParameterSample Variable A variable is any characteristics, number, or quantity that can be measured or counted. A variable may also be called a data item. Gender Age Region Height Weight Income Blood Group Ethnicity Degree Time Language
  • 20.
    Copyright © 2018,edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Basic Terminology There are a few statistical terms one should be aware of while dealing with statistics. Population ParameterSample Variable Also known as a statistical model, A statistical Parameter or population parameter is a quantity that indexes a family of probability distributions. µ ∑ х
  • 21.
    Copyright © 2018,edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Types of Analysis An analysis can be done in one of two ways. Analysis Quantitative Qualitative
  • 22.
    Copyright © 2018,edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Types of Analysis An analysis can be done in one of two ways. Also known as Statistical Analysis, it is the science of collecting & interpreting objects with numbers. Also known as Non-statistical Analysis, it mostly deals with generic data using text, media, etc Analysis Quantitative Qualitative
  • 23.
  • 24.
    Copyright © 2018,edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Inferential statistics makes inferences and predictions about a population based on a sample of data taken from the population in question. Descriptive statistics uses the data to provide descriptions of the population, either through numerical calculations or graphs or tables. Categories in Statistics There are two major categories in Statistics. Descriptive InferentialInferential
  • 25.
    Copyright © 2018,edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Descriptive Statistics This method, is mainly focused upon the main characteristics of data. It provides graphical summary of the data. Characteristics of Data Descriptive Statistics
  • 26.
    Copyright © 2018,edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Descriptive Statistics Maximum Minimum Average This method, is mainly focused upon the main characteristics of data. It provides graphical summary of the data.
  • 27.
    Copyright © 2018,edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Inferential Statistics This method, generalizes a large dataset and applies probability to draw a conclusion. It allows us to infer data parameters based on a statistical model using a sample data. Statistical Model Start Process Step Decision Answer Choice I Choice II Inferential Statistics
  • 28.
    Copyright © 2018,edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Inferential Statistics Tall Short Average This method, generalizes a large dataset and applies probability to draw a conclusion. It allows us to infer data parameters based on a statistical model using a sample data.
  • 29.
    Descriptive Statistics –Statistical Measures
  • 30.
    Copyright © 2018,edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Descriptive Statistics – Use Case Here is a sample dataset of cars containing the variables: Cars, Mileage per Gallon(mpg), Cylinder Type (cyl), Displacement (disp), Horse Power(hp) & Real Axle Ratio(drat). Using descriptive Analysis, you can analyse each of the variables in the dataset for mean, standard deviation, minimum and maximum. Cars mpg cyl disp hp drat A 21 6 160 110 3.9 B 21 6 160 110 3.9 C 22.8 4 108 93 3.85 D 21.3 6 108 96 3 E 23 4 150 90 4 F 23 6 108 110 3.9 G 23 4 160 110 3.9 H 23 6 160 110 3.9
  • 31.
    Copyright © 2018,edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Measures of the Centre There are a few statistical terms one should be aware of while dealing with statistics. Mean Median Mode
  • 32.
    Copyright © 2018,edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Descriptive Statistics – Use Case If we want to find out the average horsepower of the cars among the population of cars, we will check and calculate the average of all values. In this case, Cars mpg cyl disp hp drat A 21 6 160 110 3.9 B 21 6 160 110 3.9 C 22.8 4 108 93 3.85 D 21.3 6 108 96 3 E 23 4 150 90 4 F 23 6 108 110 3.9 G 23 4 160 110 3.9 H 23 6 160 110 3.9 110 + 110 + 93 + 96 + 90 + 110 + 110 + 110 8 = 103.625
  • 33.
    Copyright © 2018,edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Measures of the Centre There are a few statistical terms one should be aware of while dealing with statistics. Mean Median Mode Measure of average of all the values in a sample is called Mean.
  • 34.
    Copyright © 2018,edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Descriptive Statistics – Use Case If we want to find out the centre value of mpg among the population of cars, we will arrange the mpg values in ascending order to choose the middle value. In this case, 21,21,21.3,22.8,23,23,23,23 But in case of even entries, we take average of the two middle values. In this case, 22.8+23 2 = 22.9 Cars mpg cyl disp hp drat A 21 6 160 110 3.9 B 21 6 160 110 3.9 C 22.8 4 108 93 3.85 D 21.3 6 108 96 3 E 23 4 150 90 4 F 23 6 108 110 3.9 G 23 4 160 110 3.9 H 23 6 160 110 3.9
  • 35.
    Copyright © 2018,edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Measures of the Centre There are a few statistical terms one should be aware of while dealing with statistics. Mean Median Mode Measure of the central value of the sample set is called Median.
  • 36.
    Copyright © 2018,edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Descriptive Statistics – Use Case If we want to find out the most common type of cylinder among the population of cars, we will check the value which is repeated most number of times. 4 6 4 6 Cars mpg cyl disp hp drat A 21 6 160 110 3.9 B 21 6 160 110 3.9 C 22.8 4 108 93 3.85 D 21.3 6 108 96 3 E 23 4 150 90 4 F 23 6 108 110 3.9 G 23 4 160 110 3.9 H 23 6 160 110 3.9
  • 37.
    Copyright © 2018,edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Measures of the Centre There are a few statistical terms one should be aware of while dealing with statistics. Mean Median Mode The value most recurrent in the sample set is known as Mode.
  • 38.
    Copyright © 2018,edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Measures of the Spread There are a few statistical terms one should be aware of while dealing with statistics. Range Standard DeviationInter Quartile Range Variance
  • 39.
    Copyright © 2018,edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Measures of Spread There are a few statistical terms one should be aware of while dealing with statistics. Range Standard DeviationInter Quartile Range Variance Range is the given measure of how spread apart the values in a dataset are. Range = Max(𝑥𝑖) - Min(𝑥𝑖)
  • 40.
    Copyright © 2018,edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Measures of Spread There are a few statistical terms one should be aware of while dealing with statistics. Range Standard DeviationInter Quartile Range Variance Inter Quartile Range(IQR) is the measure of variability, based on dividing a dataset into quartiles. 1 2 3 4 5 6 7 8
  • 41.
    Copyright © 2018,edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Measures of Spread There are a few statistical terms one should be aware of while dealing with statistics. Range Standard DeviationInter Quartile Range Variance Quartile 1 2 3 4 5 6 7 8 Q1 Q2 Q3
  • 42.
    Copyright © 2018,edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Measures of Spread There are a few statistical terms one should be aware of while dealing with statistics. Range Standard DeviationInter Quartile Range Variance Quartile 1 2 3 4 5 6 7 8 Q1 Q2 Q3 Q1= 2+3 2 =2.5
  • 43.
    Copyright © 2018,edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Measures of Spread There are a few statistical terms one should be aware of while dealing with statistics. Range Standard DeviationInter Quartile Range Variance Quartile 1 2 3 4 5 6 7 8 Q1 Q2 Q3 Q2= 4+5 2 =4.5
  • 44.
    Copyright © 2018,edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Measures of Spread There are a few statistical terms one should be aware of while dealing with statistics. Range Standard DeviationInter Quartile Range Variance Quartile 1 2 3 4 5 6 7 8 Q1 Q2 Q3 Q3= 6+7 2 =6.5
  • 45.
    Copyright © 2018,edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Measures of Spread There are a few statistical terms one should be aware of while dealing with statistics. Range Standard DeviationInter Quartile Range Variance Inter Quartile Range 1 2 3 4 5 6 7 8 Q1 Q3
  • 46.
    Copyright © 2018,edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Measures of Spread There are a few statistical terms one should be aware of while dealing with statistics. Range Standard DeviationInter Quartile Range Variance Variance describes how much a random variable differs from its expected value. It entails computing squares of deviations.
  • 47.
    Copyright © 2018,edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Measures of Spread There are a few statistical terms one should be aware of while dealing with statistics. Range Standard DeviationInter Quartile Range Variance ❖ Deviation is the difference between each element from the mean. Deviation = (𝑥𝑖-µ)
  • 48.
    Copyright © 2018,edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Measures of Spread There are a few statistical terms one should be aware of while dealing with statistics. Range Standard DeviationInter Quartile Range Variance ❖ Population Variance is the average of squared deviations. σ² = ෍ 𝑖=1 𝑁 = (𝑥𝑖−𝜇)² 1 𝑁
  • 49.
    Copyright © 2018,edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Measures of Spread There are a few statistical terms one should be aware of while dealing with statistics. Range Standard DeviationInter Quartile Range Variance ❖ Sample Variance is the average of squared differences from the mean. s² = ෍ 𝑖=1 𝑁 = (𝑥𝑖− ҧ𝑥)² 1 (𝑛 − 1)
  • 50.
    Copyright © 2018,edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Measures of Spread There are a few statistical terms one should be aware of while dealing with statistics. Range Standard DeviationInter Quartile Range Variance Standard Deviation is the measure of the dispersion of a set of data from its mean. σ = ෍ 𝑖=1 𝑁 = (𝑥𝑖−𝜇)² 1 𝑁
  • 51.
    Copyright © 2018,edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Standard Deviation– Use Case Ross has 20 Dinosaur figures. They have the numbers 9, 2, 5, 4, 12, 7, 8, 11, 9, 3, 7, 4, 12, 5, 4, 10, 9, 6, 9, 4. Work out the Standard Deviation. Find out the mean for your sample set. STEP 1 The Mean is: 9+2+5+4+12+7+8+11+9+3+7+4+12+5+4+10+9+6+9+4 20 ⸫µ=7
  • 52.
    Copyright © 2018,edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Standard Deviation– Use Case Ross has 20 Dinosaur figures. They have the numbers 9, 2, 5, 4, 12, 7, 8, 11, 9, 3, 7, 4, 12, 5, 4, 10, 9, 6, 9, 4. Work out the Standard Deviation. Then for each number, subtract the Mean and square the result. STEP 2 (𝑥𝑖−𝜇)² (9-7)²= 2²=4 (2-7)²= (-5)²=25 (5-7)²= (-2)²=4 And so on… ⸫ We get the following results: 4, 25, 4, 9, 25, 0, 1, 16, 4, 16, 0, 9, 25, 4, 9, 9, 4, 1, 4, 9
  • 53.
    Copyright © 2018,edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Standard Deviation– Use Case Ross has 20 Dinosaur figures. They have the numbers 9, 2, 5, 4, 12, 7, 8, 11, 9, 3, 7, 4, 12, 5, 4, 10, 9, 6, 9, 4. Work out the Standard Deviation. Then work out the mean of those squared differences. STEP 3 ෍ 𝑖=1 𝑁 = (𝑥𝑖−𝜇)² 1 𝑁 4+25+4+9+25+0+1+16+4+16+0+9+25+4+9+9+4+1+4+9 20 ⸫ σ² = 8.9
  • 54.
    Copyright © 2018,edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Standard Deviation– Use Case Ross has 20 Dinosaur figures. They have the numbers 9, 2, 5, 4, 12, 7, 8, 11, 9, 3, 7, 4, 12, 5, 4, 10, 9, 6, 9, 4. Work out the Standard Deviation. Take square root of σ². STEP 4 ⸫ σ = 2.983 ෍ 𝑖=1 𝑁 = (𝑥𝑖−𝜇)² 1 𝑁 σ =
  • 55.
  • 56.
    Copyright © 2018,edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Statistics in R ❖ R is open-source and freely available. ❖ R is cross-platform compatible. ❖ R is a powerful scripting language. ❖ R is highly flexible and evolved. Reasons for moving to R
  • 57.
    Copyright © 2018,edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Statistics in R ❖ R is open-source and freely available. ❖ R is cross-platform compatible. ❖ R is a powerful scripting language. ❖ R is highly flexible and evolved. Reasons for moving to R
  • 58.
    Copyright © 2018,edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Statistics in R ❖ R is open-source and freely available. ❖ R is cross-platform compatible. ❖ R is a powerful scripting language. ❖ R is highly flexible and evolved. Reasons for moving to R
  • 59.
    Copyright © 2018,edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Statistics in R ❖ R is open-source and freely available. ❖ R is cross-platform compatible. ❖ R is a powerful scripting language. ❖ R is highly flexible and evolved. Reasons for moving to R
  • 60.
  • 61.
    Inferential Statistics –Hypothesis Testing
  • 62.
    Copyright © 2018,edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Hypothesis Testing Statisticians use hypothesis testing to formally check whether the hypothesis is accepted or rejected. Hypothesis testing is conducted in the following manner: ❖ State the Hypotheses – This stage involves stating the null and alternative hypotheses. ❖ Formulate an Analysis Plan – This stage involves the construction of an analysis plan. ❖ Analyse Sample Data – This stage involves the calculation and interpretation of the test statistic as described in the analysis plan. ❖ Interpret Results – This stage involves the application of the decision rule described in the analysis plan.
  • 63.
    Copyright © 2018,edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Hypothesis Testing Nick John Bob Harry Assume the event is free of bias. So, what is the probability of John not cheating?
  • 64.
    Copyright © 2018,edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Hypothesis Testing Nick John Bob Harry P(John not picked for a day) = 3 4 P(John not picked for 3 days) = 3 4 × 3 4 × 3 4 = 0.42 (approx) P(John not picked for 12 days) = ( 3 4 ) 12 = 0.032 < 𝟎. 𝟎𝟓
  • 65.
    Copyright © 2018,edureka and/or its affiliates. All rights reserved.www.edureka.co/masters-program/business-intelligence-certification Hypothesis Testing Nick John Bob Harry Null Hypothesis (𝑯 𝟎) : Result is no different from assumption. Alternate Hypothesis (𝑯 𝒂) : Result disproves the assumption. Probability of Event < 𝟎. 𝟎𝟓 (5%)
  • 66.
  • 67.