Data Science Course > Hypothesis Testing

Statistical Inference with Two Population in Data Science

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Statistical inference can be divided into two categories: statistical estimating and statistical hypothesis testing.

Statistical inference is the process of inferring attributes of an underlying probability distribution via data analysis. By testing hypotheses and producing estimates, inferential statistical analysis infers properties of a population.

Inferential statistics allows you to create predictions ("inferences") from data (for example, a chart or graph). Descriptive statistics describes data (for example, a chart or graph). Inferential statistics are used to generate generalisations about a population using data from samples.

Statistics can be divided into two types: (1) descriptive statistics and (2) inferential statistics.

The population of inference is the population (or universe) to which a sample survey's conclusions are supposed to generalise. Surveys are used to investigate demographic characteristics and establish broad generalisations.

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vaibhav
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best

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Anjali
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Excellent explanation

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ashif mahiboob yadwad
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Very good

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SHAIKH TOHEED
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good but practical knowledge not perfect

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PUSHPAK DILLIP MALI
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The content and teaching style is excellent. But there is slight issue is that video quality is kind of poor the dashboard is blur on 720mp on PC.

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Sayali Jadhav
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Very useful all data

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Syed Mohammad Qaiser Rizvi
5

Way of teaching is very good. I want to get PPT which he is using to teach us ?

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Anuja Bagadi
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It is a very interactive and useful course..

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All courses is very useful for which are looking free courses and gaining knowledge

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Course Content

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    7m 33s
  2. How to use LearnVern
    3m 20s

  1. Introduction to Data Measurement in Data Science
    10m 6s
  2. Types of Variables in Data Science
    2m 26s
  3. Levels of Data Measurement in Data Science
    10m 25s

  1. Charts and Graphs in Data Science
    4m 2s
  2. Frequency Distribution in Data Science
    5m 46s
  3. Bar Graphs and Pie Charts in Data Science
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  4. Summarizing Quantitative Data in Data Science
    7m 25s
  5. Cummulative Distribution in Data science
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  6. Histogram and Scatter Plot in Data Science
    7m 42s
  7. Cross Tabulation in Data Science
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  1. Measures of Central Tendency and Variability in Data Science
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  2. Mean and Percentiles in Data Science
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  3. Quartiles in Data Science
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  4. Measures of Variability in Data Science
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  5. Variance and Standard Deviation in Data Science
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  6. Application of Standard Deviation in Data Science
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  7. Z-score and Co-efficient of Variation in Data Science
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  8. Measures of Central Tendency for Grouped Data in Data Science
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  9. Standard Deviation and Variance for Grouped Data in Data Science
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  1. Skewness in Data Science
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  2. Kurtosis in Data Science
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  3. Box and Whisker Plots in Data Science
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  1. Hypothesis Testing in Data Science
    6m 33s
  2. Statistical Hypothesis in Data Science
    6m 30s
  3. Errors in Statistical Hypothesis in Data Science
    5m 45s
  4. Statistical Inference for Single Population in Data Science
    7m 11s
  5. Testing Example in Data Science
    5m 27s
  6. Statistical Inference with Two Population in Data Science
    8m 33s

  1. R Programming in Data Science
    5m 5s
  2. Data Types in R in Data Science
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  3. Understanding R Console and R Studio in Data Science
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  1. Analysis of Variance and Annova in Data Science
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  2. Assumption and Variation in One-Way ANOVA in Data Science
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  3. Two way ANOVA in Data Science
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  4. Summary of ANOVA in Data Science
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  5. ANOVA example in Data Science
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  6. One way ANOVA Example in Data Science
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  7. Two way ANOVA Example in Data Science
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  1. Understanding Datamining in Data Science
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  1. Introduction to Simple Linear Regression and Correlation in Data Science
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  2. Least Square and Residual Analysis in Data Science
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  3. Test assumption of Regression Model in Data Science
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  4. Hypothesis and Co-Efficient of Determination in Data Science
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  5. Regression Example - Creating Model in Data Science
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  6. Residual Analaysis of Model example in Data Science
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  7. Residual Analysis of Model Part 2 in Data Science
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  8. Residual Analysis of Model Part 3 in Data Science
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  1. Understanding Clustering in Data Science
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  2. Hierarchical Clustering in Data Science
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  3. Example of Hierarchial Clustering in Data Science
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  4. K Means Clustering in Data Science
    8m 6s
  5. Example of K Means Clustering in Data Science
    9m 41s

  1. Theoretical Concepts of Dashboard
    5m 41s
  2. Creating Pivots and Preparing Charts
    5m 36s
  3. Slicers in Dashboard
    3m 27s
  4. Preparing Final Dashboard
    8m 20s
  5. Presenting Dashboard
    3m 2s
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