HBX CORE FINAL EXAM
1. 1) An experiment that compares the value of a dependent variable (ex
= likelihood that a website visitor purchases an item) across two different
groups (control group and treatment group). Members of each group must be
RANDOMLY SELECTED to ensure that the only difference between the groups
is the "MANIPULATED" independent variable (ex = size of the font on two
otherwise identical websites)...a hypothesis test that tests whether the means
of the dependent variable are the same across the two groups; also used to
test whether another parameter - ex = standard deviation - is the same across
two groups: A/B Test
2. Measure of explanatory power of a regression analysis. Adjusted R squared
= R-squared*adjustment factor that decreases slightly as each independent
variable is added to regression model. Adjusted R-squared DROPS when a
new independent variable is added that does not improve the model's true
explanatory power (vs. R-squared which never decreases when a new inde-
pendent variable is added to a regression model). Adjusted R-Squared should
always be used when comparing the explanatory power of regression models
that have different numbers of independent variables.: Adjusted R-Squared
3. Theory or claim we are trying to substantiate and stated as OPPOSITE OF
A NULL HYPOTHESIS. When data allow us to nullify the null hypothesis, we
substantiate the alternative hypothesis.: Alternative Hypothesis
4. Probability distribution that is not symmetric AROUND THE MEAN: Asym-
metric Distribution
5. For a distribution with discrete values, mean = values of all data points in
the set / number of data points: Average/Mean
6. Category of categorical variable for which a dummy variable is NOT included
in a regression model. A regression model with a categorical variable that has
n categories should have n-1 dummy variables. The coefficients of the dummy
variables included in the regression model are interpreted in relation to the
base case. The analyst can select any category to be excluded from the re-
gression model; however, different base cases lead to different interpretations
of the dummy variables' coefficients. Ex: Suppose we are trying to determine
the average difference in height between men and women in a sample, and
suppose that on average men are 5 inches taller than women in the sample. If
we use Female as the base case then the coefficient for the dummy variable
for Male would be +5. If we use Male as the base case, the coefficient for the
dummy variable for Female would be -5.: Base Case
7. Tendency of a measurement process to over or under-estimate the value
of a population parameter. Although a sample statistic will almost always
differ from the population parameter, for an unbiased sample, the difference
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1. 1) An experiment that compares the value of a dependent variable (ex
= likelihood that a website visitor purchases an item) across two different
groups (control group and treatment group). Members of each group must be
RANDOMLY SELECTED to ensure that the only difference between the groups
is the "MANIPULATED" independent variable (ex = size of the font on two
otherwise identical websites)...a hypothesis test that tests whether the means
of the dependent variable are the same across the two groups; also used to
test whether another parameter - ex = standard deviation - is the same across
two groups: A/B Test
2. Measure of explanatory power of a regression analysis. Adjusted R squared
= R-squared*adjustment factor that decreases slightly as each independent
variable is added to regression model. Adjusted R-squared DROPS when a
new independent variable is added that does not improve the model's true
explanatory power (vs. R-squared which never decreases when a new inde-
pendent variable is added to a regression model). Adjusted R-Squared should
always be used when comparing the explanatory power of regression models
that have different numbers of independent variables.: Adjusted R-Squared
3. Theory or claim we are trying to substantiate and stated as OPPOSITE OF
A NULL HYPOTHESIS. When data allow us to nullify the null hypothesis, we
substantiate the alternative hypothesis.: Alternative Hypothesis
4. Probability distribution that is not symmetric AROUND THE MEAN: Asym-
metric Distribution
5. For a distribution with discrete values, mean = values of all data points in
the set / number of data points: Average/Mean
6. Category of categorical variable for which a dummy variable is NOT included
in a regression model. A regression model with a categorical variable that has
n categories should have n-1 dummy variables. The coefficients of the dummy
variables included in the regression model are interpreted in relation to the
base case. The analyst can select any category to be excluded from the re-
gression model; however, different base cases lead to different interpretations
of the dummy variables' coefficients. Ex: Suppose we are trying to determine
the average difference in height between men and women in a sample, and
suppose that on average men are 5 inches taller than women in the sample. If
we use Female as the base case then the coefficient for the dummy variable
for Male would be +5. If we use Male as the base case, the coefficient for the
dummy variable for Female would be -5.: Base Case
7. Tendency of a measurement process to over or under-estimate the value
of a population parameter. Although a sample statistic will almost always
differ from the population parameter, for an unbiased sample, the difference
1/4
