Please help me answer these questions.

DETAILS PREVIOUS ANSWERS MY NOTES ASK YOUR TEACHER PRACTICE ANOTHER DATAfile: NFLPassing A statistical program is recommended The National Football League (NFL) records a variety of performance data for individuals and teams. To investigate the importance of passing on the percentage of games won by a team, the following data show the conference (Conf), average number of passing yards per attempt (Yds/Att), the number of interceptions thrown per attempt (Int/Att), and the percentage of games won (Win%) for a random sample of 16 NFL teams for one full season Team Conf | Yds/Att Int/ Att Win% Arizona Cardinals NFC 6.5 0.042 50.0 Atlanta Falcons NFC 7.1 0.022 62.5 Carolina Panthers NFC 7.4 0.033 37.5 Cincinnati Bengals AFC 5.2 0.026 56.3 Detroit Lions NFC 7.2 0.024 62.5 Green Bay Packers NFC 8.9 0.014 93.8 Houstan Texans AFC 7.5 0.019 62.5 Indianapolis Colts AFC 5.6 0.026 12.5 Jacksonville Jaguars AFC 0.032 31.3 Minnesota Vikings NFC 5.8 0.033 18.8 New England Patriots AFC 8.3 0.020 81.3 New Orleans Saints NFC 3.1 0.021 81.3 Oakland Raiders AFC 7.6 0.044 50.0 San Francisco 49ers NFC 6.5 0.011 81.3 Tennessee Titans AFC 5.7 0.024 56.3 Washington Redskins NFC 6.4 0.041 31.3 (a) Develop the estimated regression equation that could be used to predict the percentage of games won given the average number of passing yards per attempt. (Round your numerical values to one decimal place. Let x, represent Yds/Att and y represent Win%.) = 59.6 - 0.083 X (b) Develop the estimated regression equation that could be used to predict the percentage of games won given the number of interceptions thrown per attempt. (Round your numerical values to the nearest integer. Let x, represent Int/Att, and y represent Win%.) = 94.0 - 1039x2 (c) Develop the estimated regression equation that could be used to predict the percentage of games won given the average number of passing yards per attempt and the number of interceptions thrown per attempt. (Round your numerical values to the nearest integer. Let x, represent Yds/Att, x, represent Int/Att, and y represent Win%.) d) The average number of passing yards per attempt for a certain team was 6.1 and the number of interceptions thrown per attempt was 0.034. Use the estimated regression equation developed in part (c) to predict the percentage of games won by the team. (Round your answer to one decimal place.) 43.4 For this season the team's record was 7 wins and 9 losses. Compare your prediction to the actual percentage of games won by the team. O The predicted value is higher than the actual value. The predicted value is identical to the actual value. O The predicted value is lower than the actual value.DETAILS PREVIOUS ANSWERS MY NOTES ASK YOUR TEACHER PRACTICE ANOTHER DATAfile: Showtime A statistical program is recommended. The owner of Showtime Movie Theaters, Inc., would like to predict weekly gross revenue as a function of advertising expenditures. Historical data for a sample of eight weeks follow. Weekly Gross Television Newspaper Revenue Advertising Advertising ($1,000s) ($1,000s) ($1,000s) 96 5.0 1.5 90 2.0 2.0 95 4.0 1.5 92 2.5 2.5 95 3.0 3.3 94 3.5 2.3 94 2.5 4.2 94 3.0 2.5 (a) Develop an estimated regression equation with the amount of television advertising as the independent variable. (Round your numerical values to two decimal places. Let x, represent the amount of television advertising in $1,000s and y represent the weekly gross revenue in $1,000s.) 7 = (b) Develop an estimated regression equation with both television advertising and newspaper advertising as the independent variables. (Round your numerical values to two decimal places. Let x ] represent the amount of television advertising in $1,000s, x, represent the amount of newspaper advertising in $1,0005, and y represent the weekly gross revenue in $1,000s.) (a) Develop an estimated regression equation with the amount of television advertising as the independent variable. (Round your numerical values to two decimal places. Let x, represent the amount of television advertising in $1,000s and y represent the weekly gross revenue in $1,000s.) = (b) Develop an estimated regression equation with both television advertising and newspaper advertising as the independent variables. (Round your numerical values to two decimal places. Let x] represent the amount of television advertising in $1,000s, x, represent the amount of newspaper advertising in $1,000s, and y represent the weekly gross revenue in $1,000s.) = (c) Is the estimated regression equation coefficient for television advertising expenditures the same in part (a) and in part (b)? No v , it is in part (a) and in part (b). Interpret the coefficient in each case. In part (a) it represents the change in revenue due to a one-unit increase in television advertising with newspaper advertising held constant. In part (b) it represents the change in revenue due to a one-unit increase in television advertising expenditure. In part (a) it represents the change in revenue due to a one-unit increase in television advertising expenditure. In part (b) it represents the change in revenue due to a one-unit increase in newspaper advertising with television advertising held constant. In part (a) it represents the change in revenue due to a one-unit increase in television advertising expenditure. In part (b) it represents the change in revenue due to a one-unit increase in television advertising with newspaper advertising held constant. In part (a) it represents the change in revenue due to a one-unit increase in television advertising expenditure with newspaper advertising held constant. In part (b) it represents the change in revenue due to a one-unit increase in newspaper advertising with television advertising held constant In part (a) it represents the change in revenue due to a one-unit increase in newspaper advertising expenditure with television advertising held constant. In part (b) it represents the change in revenue due to a one-unit increase in television advertising with newspaper advertising held constant. (d) Predict weekly gross revenue (in dollars) for a week when $3,100 is spent on television advertising and $1,100 is spent on newspaper advertising. (Round your answer to the nearest cent.)DETAILS PREVIOUS ANSWERS MY NOTES ASK YOUR TEACHER DATAfile: MLBPitching A statistical program is recommended Major League Baseball (MLB) pitching statistics were reported for a random sample of 20 pitchers from the American League for one full season. Player Team w ERA SO/IP | HR/IP R/IP Verlander, ] DET 2.40 1.00 0.10 0.29 Beckett, ] BOS 13 7 2.89 0.91 0,11 0.34 Wilson, C TEX 2.94 0.92 0.07 0.40 Sabathia, C NYY 19 3.00 0.97 0.07 0.37 Haren, D LAA $10 3.17 0.81 0.08 0.38 Mccarthy, B OAK 3.32 0.72 0.06 0.43 Santana, E LAA 11 3.38 0.78 0.11 0.42 Lester, ] BOS 3.47 0.95 0.10 0.40 Hernandez, F SEA 14 3.47 0.95 0.08 0.42 Buehrle, M CWS 13 3.59 0.53 0.10 0.45 Pineda, M SEA 10 3.74 1.01 0.11 0.44 Colon, B NYY 10 4.00 0.82 0.13 0.52 Tomlin, J CLE 4.25 0.54 0.15 0.48 Pavano, C MIN 13 4.30 0.46 0.10 0.55 Danks, CWS 12 4.33 0.79 0.11 0.52 Guthrie, J BAL 17 4.33 0.63 0.13 0.54 ewis, C TEX 14 10 4.40 0.84 0.17 0.51 ravally, ce'n Danks, CWS 8 12 4.33 0.79 0.11 0.52 Guthrie, J BAL 9 17 4.33 0.63 0.13 0.54 Lewis, TEX 14 10 4.40 0.84 0.17 0.51 Scherzer, M DET 15 4.43 0.89 0.15 0.52 Davis, W TB 11 10 4.45 0.57 0.13 0.52 Porcello, R DET 14 9 4.75 0.57 0.10 0.57 (a) An estimated regression equation was developed relating the average number of runs given up per inning pitched given the average number of strikeouts per inning pitched and the average number of home runs per inning pitched. What are the values of R and R ? (Round your answers to four decimal places.) R = 0.575 R. = 0.5256 (b) Does the estimated regression equation provide a good fit to the data? Explain. Considering the nature of the data, being able to explain slightly |more than 50% of the variability in the number of runs given up per inning pitched using just two independent variables is not too bad a fit vo (c) Suppose the earned run average (ERA) is used as the dependent variable in the estimated regression equation from (a) instead of the average number of runs given up per inning pitched. Does the estimated regression equation that uses the ERA provide a good fit to the data? Explain. Since more than 50% of the variability in the ERA can be explained by the linear effect of home runs per inning pitched (HR/IP) and strike-outs per inning pitched (SO/IP), this is not too bad a fit considering the complexity of predicting pitching performance.5. DETAILS PREVIOUS ANSWERS MY NOTES ASK YOUR TEACHER PRACTICE ANOTHER You may need to use the appropriate technology to answer this question. Consider the following data for a dependent variable y and two independent variables, x] and x2- * 1 * 2 30 12 92 47 10 108 25 17 112 51 16 178 40 5 94 51 19 175 74 7 170 36 12 117 59 13 142 76 16 209 The estimated regression equation for these data is = -18.06 + 2.00x, + 4.71X2- Here, SST = 15,090.1, SSR = 13,937.2, s, = 0.2495, and $ = 0.9577. (a) Test for a significant relationship among x1, X2, and y. Use a = 0.05. State the null and alternative hypotheses. OH: , # 0 and #2 = 0 Ha: 81 = 0 and #2 # 0 Ha : B, S B 2 OH: P1 = P2 = 0 He : One or more of the parameters is not equal to zero. O Ho: B, # 0 and #2 0 He: One or more of the parameters is equal to zero. OH: B B2 Ha: P1 2 P2 Find the value of the test statistic. (Round your answer to two decimal places.) Find the p-value. (Round your answer to three decimal places.) p-value = State your conclusion. O Do not reject Ho. There is sufficient evidence to conclude that there is a significant relationship among the variables. O Do not reject Ho. There is insufficient evidence to conclude that there is a significant relationship among the variables. O Reject Ho. There is sufficient evidence to conclude that there is a significant relationship among the variables. Reject Ho. There is insufficient evidence to conclude that there is a significant relationship among the variables.(b) Is /, significant? Use a = 0.05. State the null and alternative hypotheses. DHO: #1 = 0 H: 81 20 OH: 8 =0 H : 1 0 O Ho: #1 =0 Ha: 81 20 OH: 81 20 Ha: B, 50 Find the value of the test statistic. (Round your answer to two decimal places.) Find the p-value. (Round your answer to three decimal places.) p-value = State your conclusion. Do not reject Ho. There is sufficient evidence to conclude that , is significant. Do not reject Ho. There is insufficient evidence to conclude that #, is significant. O Reject Ho. There is insufficient evidence to conclude that #, is significant Reject Ho. There is sufficient evidence to conclude that #, is significant. (c) Is #, significant? Use a = 0.05. State the null and alternative hypotheses. OHo: P2 > 0 Ha: B2 SO OH: B2 + 0 Ha : P2 = 0 OH: B2 = 0 OH: BZ 0 Find the value of the test statistic. (Round your answer to two decimal places. ) Find the p-value. (Round your answer to three decimal places.) p-value = State your conclusion. Do not reject Ho. There is insufficient evidence to conclude that #, is significant. O Reject Ho. There is insufficient evidence to conclude that #2 is significant. O Do not reject Ho. There is sufficient evidence to conclude that #2 is significant. O Reject Ho. There is sufficient evidence to conclude that f, is significant