hello cab you please help me with questions d , e , h and K please

Opponent - CSU's oponnent Magazine Sales (Units) - The dependent variable, how many magazies were sold Year - The year of the sales Week In Season - The week of the football season for the sales Opponent Preseason Rank - The preseason polling rank of the opponent Preseason Ticket Sales - The number of tickes sold for that year's season Total Game Attendance - The number of fans who attended that game CSU Preseason Rank - The preseason polling rank of CSU Home Game Number - The index number for home games Conference Game ( 1= Yes; 0=No ) - Dummy variable indicating whether the game is against a conference oppoent Homecoming ( 1= Yes; 0=No ) - Dummy variable indicating whether the game is the homecoming game Game Day Weather - Sunny, Rain, or Cloudy Sunny - Dummy variable indicating Sunny weather for that game Rain - Dummy variable indicating Rainy weather for that game Kickoff Temperature - temperature observed at the beginning of the game Opponent's Previous Season Number of Wins - Number of wins opponent had in most recent season Opponent's Previous Season Number of Losses - Number of losses opponent had in most recent season CSU's Previous Season Number of Wins - Number of wins CSU had in most recent season CSU's Previous Season Number of Losses - Number of losses CSU had in most recent season Please answer questions (a) - (I) below, using the data on the "Answers" worksheet. A data dictionary has been provided on the "Data Dictionary" worksheet. Places for your answers are designated by yellow highlighted letters, corresponding to each question. (a) What type of variable(s) do we need to create to use Game Day Weather as a predictor in a regression model? (10 points) for all historical and upcoming weeks (Years 1 through 10). (10 points) (e) Conditially format the "Throwback Jersey (1= Yes; 0= No)" column with color scales. (10 points) Magazine Sales? (20 points) (g) How many opponents has CSU played in the past 9 years that had a Preseason Rank of 10 or less? (10 points) Your colleague has fit a regression model to explain Magazine Sales. See the regression model summary output on the "Regression for Magazine Sales" worksheet. (h) According to the regression model and t-test, does the "Throwback Jersey (1= Yes; 0= No)" have greater than 5% likelihood of being zero? (10 points) (i) According to the regression model coefficients, should CSU expect to sell more magazines earlier or later in a season? (20 points) (j) According to the regression model coefficients, what change in magazine sales should we expect for each decrease (i.e. from 10 to 9 ) in opponnent preseason rank? (10 points) (k) Use the regression model to make predictions for Year 10. Note that the regression model coefficients are stored below the data table, highlighted in green. (10 points) (I) If CSU sells the magazines for $20 each, how much revenue (total sales \$) does the regression model predict for year 10 ? Format the value as a US dollar (i.e. 52222.3790 should be $52,222.37 ). (20 points)