Problem 2. Now consider the regression model: qseq;= ao+awt, +4 Using just your intuition (i.e., without running any regressions in R), how do you expect to compare to B₁?
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- Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4The following fictitious table shows kryptonite price, in dollar per gram, t years after 2006. t= Years since 2006 0 1 2 3 4 5 6 7 8 9 10 K= Price 56 51 50 55 58 52 45 43 44 48 51 Make a quartic model of these data. Round the regression parameters to two decimal places.Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?
- Question 9 Assume a regression analysis yields a regression line with the value Y=$120,000 + $0.58X, where Y equals plant labor costs and X equals dollars of production output. If the company plans to produce $2,400,000 of product during the upcoming month, it would project plant labor costs to be: a. $324,000 b. $120,000 c. $204,000 d. $2,400,000Problem 2 The following printout shows the results of a simple linear regression model that predicts monthly sales (in thousands of dollars) based on how much money was spent on advertising (in thousands of dollars) during a particular month for 15 stores of a retail chain. a) Is there a statistically significant relationship between money spent on advertising and sales? Test at the 5% level of significance and explain your approach (including hypotheses). b) Somebody claims that every additional $1,000 in advertising will increase sales by more than $2,000 in the population. Can you find support for this claim given the results of your analysis? Test at the 5% level of significance and explain your approach (including hypotheses). How is this test different from the one in part a)? c) Find a 95% confidence interval for the change in sales given a $1,000 increase in the amount of money spent on advertising. How does this confidence interval relate to your answer to part a)?Question 16 Regression analysis was applied between sales (in $1000s) and advertising (in $100s), and the following regression function was obtained. = 500 + 4x Based on the above estimated regression line, if advertising is $10,000, then the point estimate for sales (in dollars) is _____. $505,000 $900 $40,500 $900,000
- If the equation of the regression line that relates hours per week spent in the tutor lab, x, to GPA, y, is y=2.1+0.28, then the best presdiction for the GPA of students who never go into the lab would be 2.1 True Or FalseA)What would the consequence be for a regression model if the errors were not homoscedastic? (B) How might you proceed if you found that (b) were actually the case?.The worker has noticed that the more time he spends at work (x), the less money he is likely to make (y) in conducting transactions for his firm. Which of the regression equations MOST suggests such a possibility?
- If you know that the equation for the simple linear regression between the final exam result and the mid-year examination result for students in the engineering statistics subject is as follows final exam: 50+0.5 x. midterm The regression coefficient is?question 26 What is the relationship between the number of minutes per day a woman spends talking on the phone and the woman's weight? The time on the phone and weight for 8 women are shown in the table below. Time 54 88 82 61 39 40 84 83 Pounds 149 198 184 166 142 140 170 163 The equation of the linear regression line is: ˆyy^ = ?+ x (Please show your answers to 3 decimal places) Use the model to predict the weight of a woman who spends 50 minutes on the phone.Weight = ? (Please round your answer to the nearest whole number.) Interpret the slope of the regression line in the context of the question: For every additional minute women spend on the phone, they tend to weigh on averge 0.87 additional pounds. As x goes up, y goes up. The slope has no practical meaning since you cannot predict a women's weight. Interpret the y-intercept in the context of the question: The y-intercept has no practical meaning for this study. The average woman's weight is…A set of X and Y scores has SSx=10,Ssy=20,and SP =8,what is the b in the regression equation?
