# Statistics homework help

Practice quiz.
Two variable relationships

1. An agent for a residential real estate company in a large city would like to be able to predict the monthly rental cost for apartments based on the size of the apartment as defined by square footage. A sample of 20 apartments in a particular residential neighborhood was selected and the data are given below.
2. Construct a scatterplot and include a title and axis labels.

1. Calculate the regression equation. Write out empirical equation representing the relationship between square feet and price of an apartment rental.

1. Interpret the meaning of the intercept (a) and the slope coefficient (b) in your equation you calculated in b. You must be very specific (labeling slope and y intercept is not an acceptable interpretation).

1. Predict the mean monthly rent for an apartment that has 1000 square feet.

1. Using the printout from your regression equation (see Part 2 on the In Lab exercise regression analysis sheet) please determine if the slope coefficient is statistically different from zero.

1. Your friends Jim and Jennifer are considering signing a lease for an apartment in this residential neighborhood. They are trying to decide between two apartments, one with 1000 square feet for a monthly rent of \$1,175 and the other with 1200 square feet for a monthly rent of \$1525. Based on your statistical analysis above (parts a-e), what would you recommend to them? Explain why.

To complete question #1, you need to turn in the excel printout of the scatterplot, and the regression equation.  Cutting and pasting these into a word document would be best.

1. (5 points) Suppose you were given from a data set the following information:

 Mean of X 45 Mean of Y 200 Standard deviation of X 12 Standard deviation of Y 35 Correlation coefficient 0.6

Calculate the regression line using the data above. (Find both the intercept and the slope).

 Apartment Size Monthly Rent (in Square feet) (in \$) 1 850 950 2 1450 1600 3 1085 1200 4 1232 1500 5 718 950 6 1485 1700 7 1136 1650 8 726 935 9 700 875 10 956 1150 11 1100 1400 12 1285 1650 13 1985 2300 14 1369 1800 15 1175 1400 16 1225 1450 17 1245 1100 18 1259 1700 19 1150 1200 20 896 1150

Data for Question #1:

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