Attach the computer regression output and demand equation based on Ln for each variable

Weekly Riders (Y) Price Data on Transit Ridership Year (X1,000) . (P) per Ride (Cents) Population (T) (X 1,000) Income (1) Parking Rate (H) (Cents) 1966 1,200 15 1967 1,800 1,190 2,900 15 1,790 1968 1,195 3,100 50 15 1,780 1969 1,110 3,200 60 25 1970 1,778 1,105 3,250 60 25 1971 1,750 1,115 3,275 25 60 1,740 1972 1,130 3,290 25 70 1973 1,095 1,725 4,100 75 Table / 30 1974 1,725 1,090 4,300 75 30 1975 1,720 1,087 4,400 75 30 1976 1,705 1,080 4,600 30 80 1977 1,710 1,020 4,815 80 40 1978 1,700 5,285 1,010 80 40 1,695 1979 5,665 85 1,010 40 1980 1,695 5,800 1,005 100 40 1,690 1981 5,900 105 995 40 1,630 1982 5,915 930 75 105 1983 1,640 6,325 915 105 75 1984 1,635 6,500 20 75 110 1985 1,630 6,612 125 940 75 1986 1,620 6,883 950 130 75 1987 1,615 910 7,005 1.50 100 1988 1,605 7,234 930 1.55 100 1989 1,590 933 7,500 165 100 1,595 1990 940 7,600 175 100 1991 1,590 7,800 948 175 100 1992 1,600 8,000 190 955 100 1,610 8,100 200 The transit manager has decided to perform a multiple regression on the data to deter- mine the impact of the rate increase. Questions: 1. Using a multiple regression program available on a computer to which you have access, estimate the coefficients of the demand model for the data given in Table 1. (Attach the computer output, and write down the regression equation) 2. Calculate the price of demand using 1992 data. 3. Calculate the income elasticity demand for 1992 data