1. From the "Mascot" dataset, define a linear model to predict whether people's attitudes toward the native american mascot (DV = attitudes) are related to their socioeconomic status (IV = SES) - a variable that you will need to create.
   • make a scale to measure "socioeconomic status" (SES), the average of the three variables: MotherEducation, FatherEducation, and Income. No reverse scoring is needed. Report the alpha reliability of the scale.
   • Graph all variables that will go into your linear model, and describe what these graphs tell you about the participants in the sample.
   • Plot the relationship between these two variables, define the linear model, and report the intercept, slope, and R2 value.
   • Next, use bootstrapping to estimate the sampling error for the slope of the model. Use this statistic to calculate the 95% Confidence Interval, and interpret what these statistics mean (do you have more or less confidence that the relationship between these two variables is not due to chance?)
   • Finally, use Null Hypothesis Significance Testing to calculate the standard error for the slope of the model. Use this statistic to calculate the 95% Confidence Interval and interpret what these statistics mean (do you have more or less confidence that the relationship between these two variables is not due to chance?)

name of the data set mascot_data

1. use one continuous DV in the dataset measured with a scale (like Extraversion or Perceived Stress), one categorical IV, and another continuous IV.
   • make a scale to measure this dependent variable. Report the alpha reliability of the scale, graph the scale as a histogram, and describe what you learn about our class' variation.
   • Graph the two independent variables - make any necessary data cleaning (i.e., scale creation, outlier or empty level removal), and describe what you learn about our class' variation for these variables.
   • Graph the relationship between the DV and IV1 (the continuous variable). Define the linear model, and report the intercept, slope, and R2 value for this model. Then, use bootstrapping and NHST to estimate the sampling error for the slope(s) of the model.
   • Plot the relationship between the DV and IV2 (the categorical variable), define the linear model, and report the intercept, slope(s), and R2 value. Then, use bootstrapping and NHST to estimate the sampling error for the slope(s) of the model. If you have more than

What is a skeleton code that can me graph answer both of these questions on studio