MGSC 291 EXAM 2 STUDY GUIDE 2026
COMPLETE QUESTIONS WITH
CORRECT DETAILED ANSWERS ||
100% GUARANTEED PASS
<RECENT VERSION>
1. what do we decide if the p val is lower than the significance level? -
ANSWER ✔ we reject the null hypothesis
2. what if the p val is greater than the significance level? - ANSWER ✔ we do
not have sufficient evidence to reject the null hypothesis
3. what is the null hypothesis? - ANSWER ✔ what we beleive to be true or
what was beleived to be common knowledge
4. what is the def of an alternaitve hypothsis - ANSWER ✔ what we want to
test, testing the opposite of the null to find out if the null is actually false
5. in a qq plot, if the points dont exactly make a perfect line, then what would
deem it normal - ANSWER ✔ a large enough sample size per the central
limit theorem
6. a confidence interval is given to find the proportion of a paramter that
belongs to a certain group
, (0.34892, 0.39242)
interpret the interval - ANSWER ✔ we are 95% confident that the
proportion lies between 34.89% and 39.24%
7. what do you do to find the confidence interval for one mean? give the R
code - ANSWER ✔ since don't have the variance, conduct t test
t.test(data$column, conf. level=0.99)
8. what do you do if want to test whether the mean we have is legit or whether
it's actually higher or lower - ANSWER ✔
t.test(data$column,mu=(x),alternative="greater")
or "less than" or "two sided"
9. A consistent estimator for the mean: - ANSWER ✔ converges on the true
parameter μ as the sample size increases.
10.pi - ANSWER ✔ is the proportion successes in a binomial distribution
11.important properties of estimators - ANSWER ✔ unbiased
efficient
consistent
12.Central Limit Theorem - ANSWER ✔ The theory that, as sample size
increases, the distribution of sample means of size n, randomly selected,
approaches a normal distribution.
np>10
, n(1-p)>10
13.Z score for confidence interval - ANSWER ✔ 1.645 1.96 2.576
14.Type 1 error - ANSWER ✔ Rejecting null hypothesis when it is true
15.type 2 error - ANSWER ✔ failing to reject a false null hypothesis
16.Use logistic regression when
a. the response is binary.
b. the response is continuous.
c. the predictor variable is binary.
d. the predictor variable is continuous. - ANSWER ✔ a. the response is
binary.
17.Making transformations of the predictor variables to create new predictor
variables is called
a. logistic regression
b. a log-log model
c. feature engineering - ANSWER ✔ c. feature engineering
18.The following R code would run which regression?
fit <- glm( y ~ .x2, data = myData)
, a. linear regression modeling y = x2
b. linear regression modeling y = -x2
c. linear regression modeling y = all other columns except x2
d. logistic regression modeling y = x1 - x2 - ANSWER ✔ c. linear
regression modeling y = all other columns except x2
19.The model log(y) ~ x is sometimes referred to as
a. a logit link
b. a log-log model
c. a log-linear model
d. a logistic regression - ANSWER ✔ c. a log-linear model
20.adding the argument family = "binomial" in the glm function in R ensures
that
a. glm runs a binomial experiment.
b. glm uses a normal error model.
c. glm runs a linear regression.
d. glm runs a logistic regression. - ANSWER ✔ d. glm runs a logistic
regression.
21.use a log-log model when
a. the predictor and response move multiplicatively with each other.
b. when the predictor and response are both binary.
c. when you want to make interpretations on the log scale. - ANSWER
✔ a. the predictor and response move multiplicatively with each other.
COMPLETE QUESTIONS WITH
CORRECT DETAILED ANSWERS ||
100% GUARANTEED PASS
<RECENT VERSION>
1. what do we decide if the p val is lower than the significance level? -
ANSWER ✔ we reject the null hypothesis
2. what if the p val is greater than the significance level? - ANSWER ✔ we do
not have sufficient evidence to reject the null hypothesis
3. what is the null hypothesis? - ANSWER ✔ what we beleive to be true or
what was beleived to be common knowledge
4. what is the def of an alternaitve hypothsis - ANSWER ✔ what we want to
test, testing the opposite of the null to find out if the null is actually false
5. in a qq plot, if the points dont exactly make a perfect line, then what would
deem it normal - ANSWER ✔ a large enough sample size per the central
limit theorem
6. a confidence interval is given to find the proportion of a paramter that
belongs to a certain group
, (0.34892, 0.39242)
interpret the interval - ANSWER ✔ we are 95% confident that the
proportion lies between 34.89% and 39.24%
7. what do you do to find the confidence interval for one mean? give the R
code - ANSWER ✔ since don't have the variance, conduct t test
t.test(data$column, conf. level=0.99)
8. what do you do if want to test whether the mean we have is legit or whether
it's actually higher or lower - ANSWER ✔
t.test(data$column,mu=(x),alternative="greater")
or "less than" or "two sided"
9. A consistent estimator for the mean: - ANSWER ✔ converges on the true
parameter μ as the sample size increases.
10.pi - ANSWER ✔ is the proportion successes in a binomial distribution
11.important properties of estimators - ANSWER ✔ unbiased
efficient
consistent
12.Central Limit Theorem - ANSWER ✔ The theory that, as sample size
increases, the distribution of sample means of size n, randomly selected,
approaches a normal distribution.
np>10
, n(1-p)>10
13.Z score for confidence interval - ANSWER ✔ 1.645 1.96 2.576
14.Type 1 error - ANSWER ✔ Rejecting null hypothesis when it is true
15.type 2 error - ANSWER ✔ failing to reject a false null hypothesis
16.Use logistic regression when
a. the response is binary.
b. the response is continuous.
c. the predictor variable is binary.
d. the predictor variable is continuous. - ANSWER ✔ a. the response is
binary.
17.Making transformations of the predictor variables to create new predictor
variables is called
a. logistic regression
b. a log-log model
c. feature engineering - ANSWER ✔ c. feature engineering
18.The following R code would run which regression?
fit <- glm( y ~ .x2, data = myData)
, a. linear regression modeling y = x2
b. linear regression modeling y = -x2
c. linear regression modeling y = all other columns except x2
d. logistic regression modeling y = x1 - x2 - ANSWER ✔ c. linear
regression modeling y = all other columns except x2
19.The model log(y) ~ x is sometimes referred to as
a. a logit link
b. a log-log model
c. a log-linear model
d. a logistic regression - ANSWER ✔ c. a log-linear model
20.adding the argument family = "binomial" in the glm function in R ensures
that
a. glm runs a binomial experiment.
b. glm uses a normal error model.
c. glm runs a linear regression.
d. glm runs a logistic regression. - ANSWER ✔ d. glm runs a logistic
regression.
21.use a log-log model when
a. the predictor and response move multiplicatively with each other.
b. when the predictor and response are both binary.
c. when you want to make interpretations on the log scale. - ANSWER
✔ a. the predictor and response move multiplicatively with each other.
