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4.2 Estimating the Coefficients of the Linear Regression Model.

3.7 Scatterplots, Sample Covariance and Sample Correlation.

3.6 An Application to the Gender Gap of Earnings.

3.5 Comparing Means from Different Populations.

3.4 Confidence Intervals for the Population Mean.

Hypothesis Testing with a Prespecified Significance Level.

Calculating the p-value When the Standard Deviation is Unknown.

Sample Variance, Sample Standard Deviation and Standard Error.

Calculating the p-Value when the Standard Deviation is Known.

3.3 Hypothesis Tests Concerning the Population Mean.

Large Sample Approximations to Sampling Distributions.

2.2 Random Sampling and the Distribution of Sample Averages.

Probability Distributions of Continuous Random Variables.

Probability Distributions of Discrete Random Variables.

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Next, you will learn about Linear Regression, which is the most popular machine learning model. Then you will learn about the primary functions of machine learning and different terms associated with machine learning. This will help you set up your system and also provide the prerequisites for building machine learning models. The course begins by explaining the basics of statistics, machine learning and the R programming language. This free online course Diploma in Machine Learning with R studio explains how machine learning (ML) techniques can be used to solve business problems.