1) Listed below are paired data consisting of amounts spent on advertising (in millions of dollars) and the profits (in millions of dollars). Determine if there is a significant negative linear correlation between advertising cost and profit . Use a significance level of 0.01 and round all values to 4 decimal places. Advertising Cost Profit 3 15 4 29 5 16 6 30 7 21 8 27 9 21 10 27 11 22 12 24   Ho: ρ = 0 Ha: ρ < 0 Find the Linear Correlation Coefficient     r =  Find the p-value    p-value =    The p-value is Greater than αα Less than (or equal to) αα   The p-value leads to a decision to Do Not Reject Ho Accept Ho Reject Ho   The conclusion is  There is insufficient evidence to make a conclusion about the linear correlation between advertising expense and profit. There is a significant linear correlation between advertising expense and profit. There is a significant positive linear correlation between advertising expense and profit. There is a significant negative linear correlation between advertising expense and profit.

The data below is 12 observations of Math SAT scores (x) and scores on Math placement test (y). Calculate the linear correlation coefficient, rr. Enter your answers to two decimal places. X Y 420 426 410 360 600 575 460 538 550 553 480 407 560 635 450 388 400 383 430 402 470 462 440 437 rr =

A random sample of college students was surveyed about how they spend their time each week.  The scatterplot below displays the relationship between the number of hours each student typically works per week at a part- or full-time job and the number of hours of television each student typically watches per week.  The correlation between these variables is r = –0.63, and the equation we would use to predict hours spent watching TV based on hours spent working is as follows:             Predicted hours spent watching TV = 17.21 – 0.23(hours spent working) Since we are using hours spent working to help us predict hours spent watching TV, we’d call hours spent working a(n) __________________ variable and hours spent watching TV a(n) __________________ variable. The correlation coefficient, along with what we see in the scatterplot, tells us that the relationship between the variables has a direction that is _________________ and a strength that is ______________________. According to the…

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What can be said about the relationship between the cost of advertising and the number of products sold? A. Strong negative correlation B. very strong negative correlation. C. Strong positive correlation. D. Very strong positive correlation

Listed below are paired data consisting of movie budget amounts and the amounts that the movies grossed. Find the regression​ equation, letting the budget be the predictor​ (x) variable. Find the best predicted amount that a movie will gross if its budget is ​$135 million. Use a significance level of alpha equals 0.05 . Budget left parenthesis $ right parenthesis in Millions 45 24 115 74 72 48 117 67 4 60 124 24 6 152 8   Gross left parenthesis $ right parenthesis in Millions 115 10 95 69 127 112 93 101 50 102 223 26 18 282 56   The regression equation is ŷ = __ + __x. (Round to one decimal place as needed.)

Y 5 10 15 20 25 30 -0.112 0.001 0.091 0.942 0 Based on the plot displayed here, what is a possible correlation between the variables and y?