I need help answering these questions. I get incorrect answers

A company publishes restaurant ratings for various locations. The accompanying data table contains the summated rating forfood, dcor,service, and cost per person for a sample of 50 restaurants located in a city and 50 restaurants located in a suburb. Develop a regression model to predict the cost perperson, based on the summated rating variable and a dummy variable concerning location(city vs.suburban).

Complete parts(a) through(f). For(a) through(d), do not include an interaction term.

Summated_Rating Cost_($) Location

60 65 City

68 67 City

50 26 City

74 80 City

52 34 City

48 39 City

64 45 City

55 41 City

56 39 City

48 46 City

65 43 City

55 29 City

66 57 City

57 58 City

53 31 City

69 57 City

51 25 City

49 40 City

61 43 City

51 44 City

62 39 City

58 32 City

67 55 City

53 44 City

57 47 City

61 25 City

51 35 City

68 79 City

54 44 City

42 19 City

57 39 City

62 48 City

55 46 City

64 51 City

68 61 City

57 50 City

49 38 City

63 31 City

67 51 City

50 27 City

60 46 City

65 59 City

61 66 City

68 57 City

54 64 City

60 61 City

54 46 City

73 76 City

58 64 City

54 44 City

60 53 Suburban

61 43 Suburban

50 39 Suburban

57 40 Suburban

63 44 Suburban

51 27 Suburban

65 38 Suburban

59 37 Suburban

56 32 Suburban

52 39 Suburban

61 53 Suburban

59 28 Suburban

58 50 Suburban

52 43 Suburban

51 33 Suburban

64 53 Suburban

56 48 Suburban

52 36 Suburban

59 27 Suburban

66 43 Suburban

57 40 Suburban

62 38 Suburban

70 53 Suburban

65 43 Suburban

50 31 Suburban

55 44 Suburban

56 42 Suburban

53 46 Suburban

69 39 Suburban

64 42 Suburban

56 36 Suburban

65 56 Suburban

75 62 Suburban

60 47 Suburban

49 30 Suburban

60 37 Suburban

66 71 Suburban

64 37 Suburban

60 48 Suburban

59 34 Suburban

55 28 Suburban

56 45 Suburban

57 26 Suburban

57 42 Suburban

48 35 Suburban

70 63 Suburban

64 35 Suburban

46 23 Suburban

64 53 Suburban

64 60 Suburban

a. State the multiple regression equation that predicts the cost per person, based on the summatedrating, X1, and thelocation, X2. Define X2 to be 0 for restaurants located in a city and let X2 be 1 for restaurants located in a suburb.

Yi= ( )+( )X1i+( )X2i

(Round to three decimal places asneeded.)

b. Interpret the regression coefficients in(a).

Holding constant whether a restaurant is in a city or asuburb, for each increase of 1 unit in the summatedrating, the predicted cost per person is estimated to change by dollars. Holding constant the summatedrating, the presence of the restaurant in a (city OR suburb) is estimated to decrease the predicted cost per person by (1.219 OR 24.247 OR 10.014 OR 5.805) dollars over the cost per person of a restaurant in a (suburb OR city)

(Round to three decimal places asneeded.)

c. At the 0.05 level ofsignificance, determine whether each independent variable makes a contribution to the regression model.

Test the first independentvariable, SummatedRating. Determine the null and alternative hypotheses.

H0: 1

H1: 1

The test statistic for the first independentvariable, SummatedRating, is

tSTAT=

(Round to three decimal places asneeded.)

Thep-value for the first independentvariable, SummatedRating, is

(Round to four decimal places asneeded.)

Since thep-value is (greater OR less) than the value of , (do not reject OR reject) the null hypothesis. The first independentvariable, SummatedRating, (appears OR does not appear) to make a contribution to the regression model.

Test the second independentvariable, Location. Determine the null and alternative hypotheses.

H0: 2

H1: 2

The test statistic for the second independentvariable, Location, is

tSTAT=

(Round to three decimal places asneeded.)

Thep-value for the second independentvariable, Location, is

(Round to four decimal places asneeded.)

Since thep-value is (greater OR less) than the value of , (reject OR do not reject) the null hypothesis. The second independentvariable, Location, (appears OR does not appear) to make a contribution to the regression model.

d. Construct and interpret a95% confidence interval estimate of the population slope of the relationship between Cost and SummatedRating.

Taking into account the effect of (Summated Rating OR Cost OR Location) the estimated effect of a1-unit increase in SummatedRating is to change the (Location OR Cost OR summated Rating) by to dollars.

(Round to three decimal places as needed. Use ascendingorder.)