We have 2 variables Response variable = Final grade -- Quantit.pdf
We have 2 variables: Response variable = Final grade <-- Quantitative (specifically \"interval\") Explanatory variable = Gender <-- Categorical (specifically \"nominal\") * To numerically describe the final grades... If the data is symmetric, we want to use the MEAN and STANDARD DEVIATION to describe the center and spread of the observations respectively. If the data is skewed, we want to use the MEDIAN and IQR to describe the center and spread of the observations respectively. * To graphically describe the final grades... We can either construct (1) separate histograms based on gender using the same classes, and/or (2) side-by-side boxplots. Solution We have 2 variables: Response variable = Final grade <-- Quantitative (specifically \"interval\") Explanatory variable = Gender <-- Categorical (specifically \"nominal\") * To numerically describe the final grades... If the data is symmetric, we want to use the MEAN and STANDARD DEVIATION to describe the center and spread of the observations respectively. If the data is skewed, we want to use the MEDIAN and IQR to describe the center and spread of the observations respectively. * To graphically describe the final grades... We can either construct (1) separate histograms based on gender using the same classes, and/or (2) side-by-side boxplots..
We have 2 variables Response variable = Final grade -- Quantit.pdf
We have 2 variables: Response variable = Final grade <-- Quantitative (specifically \"interval\") Explanatory variable = Gender <-- Categorical (specifically \"nominal\") * To numerically describe the final grades... If the data is symmetric, we want to use the MEAN and STANDARD DEVIATION to describe the center and spread of the observations respectively. If the data is skewed, we want to use the MEDIAN and IQR to describe the center and spread of the observations respectively. * To graphically describe the final grades... We can either construct (1) separate histograms based on gender using the same classes, and/or (2) side-by-side boxplots. Solution We have 2 variables: Response variable = Final grade <-- Quantitative (specifically \"interval\") Explanatory variable = Gender <-- Categorical (specifically \"nominal\") * To numerically describe the final grades... If the data is symmetric, we want to use the MEAN and STANDARD DEVIATION to describe the center and spread of the observations respectively. If the data is skewed, we want to use the MEDIAN and IQR to describe the center and spread of the observations respectively. * To graphically describe the final grades... We can either construct (1) separate histograms based on gender using the same classes, and/or (2) side-by-side boxplots..
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We have 2 variables Response variable = Final grade -- Quantit.pdf
- 1. We have 2 variables: Response variable = Final grade <-- Quantitative (specifically "interval") Explanatory variable = Gender <-- Categorical (specifically "nominal") * To numerically describe the final grades... If the data is symmetric, we want to use the MEAN and STANDARD DEVIATION to describe the center and spread of the observations respectively. If the data is skewed, we want to use the MEDIAN and IQR to describe the center and spread of the observations respectively. * To graphically describe the final grades... We can either construct (1) separate histograms based on gender using the same classes, and/or (2) side-by-side boxplots. Solution We have 2 variables: Response variable = Final grade <-- Quantitative (specifically "interval") Explanatory variable = Gender <-- Categorical (specifically "nominal") * To numerically describe the final grades... If the data is symmetric, we want to use the MEAN and STANDARD DEVIATION to describe the center and spread of the observations respectively. If the data is skewed, we want to use the MEDIAN and IQR to describe the center and spread of the observations respectively.
- 2. * To graphically describe the final grades... We can either construct (1) separate histograms based on gender using the same classes, and/or (2) side-by-side boxplots.