Quantitative Research Methods

EC315 Quantitative Research Methods


PROBLEM 1 (15 pts):  A brand of fluorescent light tubes is advertised as having an average illumination life-span of 2,000 hours.  A random sample of 64 bulbs burned out with a mean life-span of 1,970 hours and a sample standard deviation of 80 hours.  With a 95% level of significance, test the claim that the advertised mean equals 2,000.









PROBLEM 2 (15 pts):  Given the following data from two independent data sets, conduct a one-tail hypothesis test to determine if the means are statistically equal using an alpha of 0.05.

n1 = 45        n2 = 36

xbar1= 64    xbar2 = 60

s1=15       s2 = 12








PROBLEM 3 (15 points): A test was conducted to determine if the gender of a spokesperson affected the likelihood that consumers would prefer a new product.  A survey at a trade show employing a female spokesperson determined that 60 out of 200 customers preferred the product, while 72 of 180 customers preferred the product when a male spokesperson was employed.  At the 0.05 level of significance, do the samples provide sufficient evidence to indicate that, in this situation, the gender of the spokesperson matters to consumers?









PROBLEM 4 (15 points):  Assume the population variances are equal for Male and Female GPA’s.  Test the sample data to see if Male and Female PhD candidate GPA’s (Means) are equal.   Conduct a two-tail hypothesis test, α =.05.

  Male GPA’s Female GPA’s
Sample Size 17 15  
Sample Mean 3.8 3.95  
Sample Standard Dev .5 .7  







PROBLEM 5 (20 pts):  The Neutra-Pride Bottling Company wants to determine if their soda has appeal to the general public.  A sample of 500 individuals were selected and asked to indicate the soda they preferred.  Each volunteer sampled 5 brands of soda.  Conduct a Hypothesis test at α =.05 to determine if the preference of sodas is uniform (or if there appears to be some preference).


Brand of Soda    Observed (fo)

A                            115

B                               80

C                             105

D                            130

E                                            70



PART II REGRESSION ANALYSIS (40 points)A real estate investor has devised a simple model to estimate home prices in a fancy, new suburban development.  Data from a random sample of 32 homes were gathered on the selling price of the home ($ thousands), the home size (in square feet), the lot size (in square feet), and the number of bedrooms. The following Excel Output Summary was generated:


Regression Statistics      
Multiple R 0.9647      
R Square 0.9307      
Adjusted R Square 0.9227      
Standard Error 26.0389      
Observations 32      
  Coefficients Standard Error t Stat P-value
Intercept 34.6165 38.3735 -0.9021 0.3753
X1 (Square Feet) 0.1232 0.0184 8.3122 0.0018
X2 (Lot Size)   .00142 1.7120 5.2583 0.0122
X3 (Bedrooms) 17.3903 6.8905 2.5238 0.1259


Answer the following questions, and note that some are two-parters, so answer both!

  1. Is there a linear relationship in this model?  How can you tell?


  1. Why is the coefficient for lot size a positive number? What is the value (per square foot) of a lot?


  1. Which is the most statistically significant variable?  What evidence shows this?


  1. Which is the least statistically significant variable?  What evidence shows this?


  1. For a 0.05 level of significance, could any variable(s) be dropped from this model?  Why?


  1. Looking at the associated p-value, should the Intercept be dropped from this model? Why?


  1. Using all of the variables: What should be the cost of a 2,000 square-foot home with a lot size of 5,000 square feet and three bedrooms?







Problem 1:  In a regression analysis:


  1. What is the range of an R2 value? ________________________


  1. What does or could a p-value = 0.18 mean/imply? __________________________________


  1. What is a Durbin-Watson test designed to detect? _________________________________


  1. How is an Adjusted R2 value different from an R2 value? ___________________________


Problem 2:  Define multicollinearity in the following terms:


  1. Variables need to be ___________________ in a linear regression model.


  1. In which type of regression is multicollinearity likely to occur? ________________________


  1. What is the negative impact of multicollinearity in a regression? ______________________


  1. How could you identify if it exists? _____________________________________


  1. If multicollinearity is found in a regression, how is it eliminated? ___________________



Problem 3:  The mean (average) is an indicator of central tendency.  However, it tells us nothing about the variation in our population.  What is used as a good indicator of variation?    _____________



Problem 4:  The game is tied at the sound of the horn…but wait…Tim Duncan has been fouled and will have a chance to shoot the winning basket.   During past games, Tim has only made 12 out of 28 free-throws.  Based on past experience: What is the probability he will miss the shot and the game will go into overtime?   _____________



Problem 5:  Using a standardized test, Ms. Parker determined the average reading score for her third grade students was 72 with a standard deviation of 12.  Timmy got a 58 on the test.


  1. Should Ms. Parker be concerned about Timmy’s reading ability? Why (or why not)?



  1. What percentage of students did worse than Timmy on this standardized test?



  1. Brenda’s Mom bragged to Timmy’s Mom that her daughter scored a 96. Could her claim be valid? Why?  (Hint: Compute the Z-value or use a 95% Confidence interval) Would you conclude Brenda’s Mom is a jerk?

Last Updated on March 25, 2019 by Essay Pro