I need help answering these questions. I keep getting 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 61 City

68 65 City

50 20 City

74 77 City

52 33 City

48 38 City

64 45 City

55 43 City

56 40 City

48 41 City

65 47 City

55 31 City

66 57 City

57 57 City

53 33 City

69 58 City

51 23 City

49 39 City

61 46 City

51 46 City

62 43 City

58 33 City

67 60 City

53 43 City

57 50 City

61 30 City

51 32 City

68 78 City

54 43 City

42 24 City

57 40 City

62 51 City

55 47 City

64 55 City

68 67 City

57 46 City

49 42 City

63 32 City

67 53 City

50 29 City

60 42 City

65 58 City

61 71 City

68 61 City

54 63 City

60 61 City

54 47 City

73 77 City

58 66 City

54 40 City

60 53 Suburban

61 47 Suburban

50 42 Suburban

57 45 Suburban

63 44 Suburban

51 32 Suburban

65 40 Suburban

59 32 Suburban

56 34 Suburban

52 36 Suburban

61 56 Suburban

59 31 Suburban

58 52 Suburban

52 43 Suburban

51 36 Suburban

64 56 Suburban

56 50 Suburban

52 35 Suburban

59 30 Suburban

66 41 Suburban

57 40 Suburban

62 37 Suburban

70 54 Suburban

65 41 Suburban

50 33 Suburban

55 44 Suburban

56 38 Suburban

53 45 Suburban

69 39 Suburban

64 44 Suburban

56 38 Suburban

65 57 Suburban

75 61 Suburban

60 47 Suburban

49 30 Suburban

60 35 Suburban

66 69 Suburban

64 37 Suburban

60 51 Suburban

59 35 Suburban

55 26 Suburban

56 46 Suburban

57 24 Suburban

57 41 Suburban

48 34 Suburban

70 62 Suburban

64 36 Suburban

46 25 Suburban

64 55 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 (16.159 OR 1.247 OR 25.264 OR 5.899) 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 (less OR greater) than the value of , (reject OR do not 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 , (do not reject OR reject)

the null hypothesis. The second independentvariable, Location, (does not appear OR appears) 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 (Summated Rating OR Location OR Cost) by to dollars.

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