Statistics for Marketing Coursera Quiz Answers – Networking Funda

All Weeks Statistics for Marketing Coursera Quiz Answers

This course takes a deep dive into the statistical foundation upon which Marketing Analytics is built. The first part of this course is all about getting a thorough understanding of a dataset and gaining insight into what the data actually means.

The second part of this course goes into sampling and how to ask specific questions about your data. Finally, the third part is about answering those questions with analyses. Many of the mistakes made by Marketing Analysts today are caused by not understanding the concepts behind the analytics they run, which causes them to run the wrong test or misinterpret the results.

This course is specifically designed to give you the background you need to understand what you are doing and why you are doing it on a practical level.

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Statistics for Marketing Week 01 Quiz Answers

Practice Quiz: Measures of Central Tendency

Q1. Which of the following is NOT a measure of central tendency?

  • Variance
  • Mean
  • Median
  • Mode

Q2. Based on the video, what are measures of central tendency?

  • They identify which number in the dataset is the middle one.
  • They are numbers that represent the “middle” of a dataset.
  • They are numbers that tell you how accurate the data is.
  • They are numbers that represent how spread out the data is in a dataset.

Q3. How might a marketer find value in knowing the middle of a dataset?

I. It can help predict future sales numbers.

II. It can help assess the impact of marketing efforts.

III. It can provide a reasonable baseline for what to expect from a particular demographic.

  • II and III.
  • I and III.
  • III only.
  • I, II, and III.

Q4. What is the mean of the following set of numbers?

-11, 2, -2, 0, 9, 8

  • The mean is 0.
  • The mean is -11.
  • The mean is 1.
  • The mean is 6.

Q5. Which measure of central tendency would be the most reasonable for defining the middle of the dataset below?

-2, 4, 1, -1, 3, 73

  • Mean, median, or mode is suitable.
  • The median.
  • The mean.
  • The mode.

Q6. Calculate the median for the following set of numbers.

-4, 1, 7, -2, 48, 22, -11

  • The median is 1.
  • The median is 48.
  • The median is -2.
  • The median is 0.

Q7. In a spreadsheet, the following formula will find the mode of the numbers in the cells A1 to A1000.

=(MODE A1:A1000)

  • T​rue
  • F​alse

Q8. What is an advantage the median has over the mean?

  • It better represents the middle for large datasets.
  • It better represents a typical value when the values in the dataset are all close to each other.
  • It handles negative numbers better.
  • The median is insensitive to outliers.

Q9. What is the mode for the following set of numbers?

5, 3, -2, 44, 3, 44, 3

  • The mode is 44.
  • The mode is 3.
  • There is no mode.
  • The modes are 3 and 44.

Practice Quiz: Measures of Dispersion

Q1. What is the purpose of a measure of variation?

  • It provides an indication of how spread out the data is.
  • It determines the size of the dataset.
  • It gives an idea of where the middle of the data is.
  • It determines the accuracy of the data.

Q2. Measures of variation is an alternative name for measures of central tendency.

  • False
  • True.

Q3. Which of the following are examples of measures of variation?

I. Range

II. Standard deviation

III. Mean

  • I and II
  • I and III
  • II and III
  • All three.

Q4. What is the range for the following dataset?

2, 3, -5, 8, 0, -2

  • 8
  • 5.5
  • 13
  • -5

Q5. Suppose that you are given the following data. Would the range be a reasonable measure of the spread in the data? Why or why not?

2, 1, 5, 10, 6, 45

  • Yes, it is reasonable because the range is positive.
  • No, it is not reasonable because there is an outlier.
  • No, it is not reasonable because the range is too large.
  • Yes, it is reasonable because there are no negative numbers in the dataset.

Q6. What is the formula used to find the z-score for a data value?

  • z = (value + mean)/std.
  • z = value – mean/std.
  • z = (value – std)/mean.
  • z = (value – mean)/std.

Q7. For a normal distribution how many data values should you expect to fall within two standard deviations of the mean?

  • 47.6%
  • 95.2%
  • 13.6%
  • 68%

Q8. How can a z-score be useful?

I. It can help in determining if a data value is high or low in the population.

II. It tells you how many standard deviations from the mean a data value is.

III. It can be used to determine data outliers.

  • All three are ways a z-score can be useful.
  • I and II
  • II and III
  • I and III

Graded Quiz: Descriptive Statistics

Q1. What is the formula used to find the z-score for a data value?

  • z = value + mean/std.
  • z = (value – mean)/std.
  • z = (mean – std)/value.
  • z = value – mean.

Q2. What is the formula for finding the median of a dataset in a spreadsheet? (Assume that the data is in the cells A1 to A100.)

  • MEDIAN(A1:A100)
  • =MEDIAN(A1:A100)
  • =MEDIAN_A1:A100
  • =MDN(A1:A100)

Q3. What is the range for the given dataset? 23, 20, 31, 11, 15, 19

  • 23
  • -20
  • 20
  • 11

Q4. What is the mode for the following set of numbers? 26, 11, 45, 0, 7, 7, 0

  • The mode is 7.
  • The modes are 7 and 0.
  • The mode is 0.
  • The mode is 45.

Q5. In a normal distribution, what percentage of data values are found between one and two standard deviations above the mean?

  • 95.2%
  • 68%
  • 50%
  • 13.6%

Q6. What is the median for the given dataset? 19, 42, 33, 15, 21

  • The median is 15.
  • The median is 33.
  • The median is 21.
  • The median is 45.

Q7. True or false: The median is sensitive to outliers.

  • False
  • True

Q8. What is the mean of the given set of numbers? 10, 20, 30, -15, -25, -20

  • The mean is 0.
  • The mean is 30.
  • The mean is -25.
  • The mean is 10.

Q9. Which of the following is not a use for a measure of variation? I. Data reliability II. Summarizing the data in a number III. Risk analysis IV. All of these

  • II
  • III
  • I
  • IV

Q10. What do measures of central tendency tell you about a dataset? I. They represent how spread out the data is II. They represent the middle of the dataset III. They tell you how large the dataset is

  • III
  • I, II, and III
  • II
  • I

Statistics for Marketing Week 02 Quiz Answers

Practice Quiz: Sampling

Q1. What is the difference between a population and a sample?

  • A sample includes every individual in a group, while a population is a subset of a sample used to represent a group.
  • A population includes every individual in a group, while a sample is a subset of a population used to represent a group.
  • A population involves people, but a sample does not.
  • These terms are synonymous.

Q2. Which of the following are conclusions of the Central Limit Theorem? I. Large samples should have the same mean and standard deviation as the population. II. Large samples normalize data. III. The accuracy of data analysis will always increase as more data is collected.

  • I and II
  • I and III
  • II and III
  • All of these are conclusions of the theorem.

Q3. True or false: When choosing a sample, size doesn’t matter.

  • False
  • True

Q4. What does the plateau effect state?

  • That larger populations yield more reliable results from analysis.
  • That larger sample sizes are always better.
  • That after a certain point, adding more data to the sample will not increase accuracy.
  • That all sample sizes are acceptable in performing analysis.

Q5. What are the four types of sampling techniques introduced in the lesson? I. Simple random, systematic, stratified, cluster II. Simple random, systematic, fast random, cluster III. Simple random, complex random, cluster, point-wise

  • I
  • II
  • III
  • None of these

Q6. What type of sampling is illustrated in the following scenario? A pollster wants to gauge public support for a new initiative. He decides to phone every fourth person listed in the phone book.

  • This is an example of simple random sampling.
  • This is an example of stratified sampling.
  • This is an example of cluster sampling.
  • This is an example of systematic sampling.

Q7. A company has worksites across the country. Each of the worksites has roughly the same number of employees in similar roles. The company wants to sample their workers, but they are unable to visit all sites to collect their data. How should they go about sampling their population of employees?

  • Simple random sampling
  • Systematic sampling
  • Stratified sampling
  • Cluster sampling

Practice Quiz: Distributions

Q1. How can knowing the probability distribution of customer data help a marketer? I. It can be used to predict future customer behavior II. It can be used to determine the purchase history of specific customers III. It can inform a marketer about customer preferences

  • II and III
  • I, II, and III
  • I and II
  • I and III

Q2. How does data with a high variance affect the shape of a distribution?

  • The distribution will have a short, wide shape.
  • The distribution will have a short, narrow shape.
  • The distribution will have a tall, wide shape.
  • The distribution will have a narrow, tall shape.

Q3. What does a narrow and tall distribution tell you about the variance of the underlying data?

  • That the dataset has low variance.
  • That the variance is increasing
  • That the variance is not important.
  • That the dataset has high variance.

Q4. Which of the following is not a type of distribution discussed in the lesson? I. Positive II. Exponential III. Poisson

  • I
  • II
  • III
  • All of these

Q5. True or false: The following are the four transformations discussed in the lesson: Cube Root, Square, Square Root, Logarithmic

  • True
  • False

Q6. What does kurtosis describe?

  • It describes how symmetric the data is about the mean.
  • It describes how the data may be pulled to the left or the right.
  • It describes how data may be pulled up or down relative to the normal distribution.
  • It describes how wide the data is.

Q7. What does skew describe?

  • It describes how the data may be pulled to the left or the right.
  • It describes how wide the data is.
  • It describes how sharp the peak of the data is when compared to the normal distribution.
  • It describes how data may be pulled up or down relative to the normal distribution.

Practice Quiz: Variable Types

Q1. What is a quantitative variable?

  • It is a variable that represents a numerical value that can be interpreted mathematically.
  • It is a variable that represents numbers that can be counted.
  • It is a variable that represents only large numbers.
  • It is numerical data that is used for labeling.

Q2. Which of the following are examples of quantitative variables?

I. Sales data

II. Jacket colors

III. Ticket prices

  • I and III
  • II and III
  • I and II
  • All of these are quantitative data types.

Q3. Which of the following are examples of qualitative variables?

I. The amount earned by a charity drive

II. The order of individuals in a list

III. Yelp ratings

  • All of these are qualitative data types.
  • II and III
  • I and III
  • I and II

Q4. Quantitative variables can be broken down into two categories. What are they?

  • Nominal and ordinal
  • Discrete and continuous
  • Discrete and nominal
  • Ordinal and continuous

Q5. True or false: The color of a ball is an example of a nominal variable.

  • False
  • True.

Q6. There are two categories of qualitative variables. What are they?

  • Nominal and ordinal
  • Discrete and nominal
  • Discrete and continuous
  • Ordinal and continuous

Q7. True or false: The count of cars in a parking lot is an example of a discrete variable.

  • True.
  • False

Graded Quiz: Sampling, Distribution, and Variables

Q1. How does the skew affect the shape of the graph of a distribution?

  • Skew determines how tall the graph is.
  • Skew determines how wide the data is.
  • Skew will cause the graph to lean either to the left or right.
  • Skew determines how narrow the graph is.

Q2. What sampling technique is depicted in this scenario? A restaurant wants to add a new menu item. The cost of the item to the restaurant is significant, so they only are interested in doing it if they believe that the customers will purchase the dish. They survey the first 20 customers who enter the restaurant each day for a month for feedback.

  • Clustering
  • Simple random
  • Systematic
  • Stratified

Q3. True or false: Transformations cannot correct for kurtosis.

  • False
  • True

Q4. What is a quantitative variable?

  • A variable that represents quality.
  • A variable that represents a quantity.
  • A variable that represents numerical rank.
  • A variable that uses numbers as labels.

Q5. What is a qualitative variable?

  • A variable that represents the size of an object.
  • A variable that is numeric.
  • A variable that represents a quality of an object.
  • A variable that represents a quantity.

Q6. Which is a subtype of quantitative variables?

  • Ordinal
  • Nominal
  • Continuous
  • Random

Q7. What is the difference between independent and dependent variables?

  • Dependent variables can be controlled in a test or experiment while independent variables are measured.
  • Independent variables are quantitative, while dependent variables are qualitative.
  • Dependent variables are quantitative, while independent variables are qualitative.
  • Independent variables can be controlled in a test or experiment while dependent variables are measured.

Q8. Which one of the following is a subtype of qualitative variables?

  • Continuous
  • Nominal
  • Random
  • Discrete

Q9. What is the uniform distribution?

  • A distribution that gives the probability of something happening based on the number of times something else has happened.
  • A distribution that is shaped like a bell.
  • A distribution in which all values have an equal chance of happening.
  • A distribution that depends on time.

Statistics for Marketing Week 03 Quiz Answers

Practice Quiz: Experimental Design and Hypotheses

Q1. In the context of this course, what is an evaluation question?

  • A question about the results of an experiment.
  • A question that motivates a study or experiment.
  • A question about the accuracy of an experiment.

Q2. What are possible sources for evaluation questions? I. Data II. Stakeholders III. Managers

  • II
  • All three.
  • I
  • III

Q3. Which of the following is/are parts of a hypothesis? I. What will change? II. How will it change? III. What will cause the change?

  • I. and II.
  • All three are the parts of a hypothesis.
  • II. and III.
  • I. and III.

Q4. What is a hypothesis?

  • It is a tentative answer to the evaluation question.
  • It is a question that motivates a study.
  • It is another name for a theory.
  • It is the result of a statistical analysis.

Q5. What are the two types of studies used to test a hypothesis?

  • Data driven and actual.
  • Experimental and actual.
  • Observational and simple.
  • Observational and experimental.

Q6. Which of these is an example of an observational study?

  • Data mining.
  • Random trials.
  • Repeated measures.
  • A/B test.

Q7. True or false: A/B testing is an example of an experimental study.

  • False
  • True

Q8. What are the five steps in experimental design?

  • Question, hypothesis, define variables, measurement, and analysis.
  • Prediction, question, hypothesis, define variables, and analysis.
  • Question, hypothesis, measurement, solving, and analysis.
  • Question, theory, define variables, measurement, and analysis.

Practice Quiz: Hypothesis and AB Testing

Q1. What is AB testing?

  • A test to determine if the analysis has bias.
  • A test to determine the stakeholders of an analysis.
  • A study that compares two different versions of something to see which performs better.
  • A study that compares two unrelated things to see which performs better.

Q2. When performing AB testing, the alternate hypothesis H1 makes what claim?

  • That there is a significant difference in the effect between A and B.
  • That A performs better than B.
  • That A and B perform the same or similar.
  • That there is no significant difference in the effect between A and B.

Q3. In general, what do you conclude for a p-value where p > 0.05?

  • Reject H0, reject H1.
  • Accept H0, accept H1.
  • Reject H0, accept H1.
  • Accept H0, reject H1.

Q4. What does a 95% confidence interval represent?

  • The chance of making a type 1 error.
  • The range of values within which you may be 5% sure that the true mean falls.
  • The chance of making a type 2 error.
  • The range of values within which you may be 95% sure that the true mean falls.

Q5. Which common hypothesis tests were mentioned in the video?

I. t-test

II. ANOVA

III. Chi-squared

  • III.
  • I.
  • II.
  • All of these.

Q6. True or false: The correct syntax for using a t-test in a spreadsheet is:

=ttest(Group, Tails, Type)

  • True.
  • False.

Graded Quiz: Experimental Design and Testing

Q1. What is a type 2 error?

  • When the alternate hypothesis is falsely accepted.
  • When the null is falsely accepted.
  • When the null hypothesis is falsely rejected.
  • When both the null and alternate hypotheses are rejected.

Q2. Which of the following are steps in experimental design?

I. Measurement

II. Hypothesis

III. Communicate

  • I. and II.
  • II. and III.
  • I. and III.
  • All of these.

Q3. A retail store is considering increasing their current discount on a product to see if it will sell more. They decide to perform an AB test. What is the alternate hypothesis?

  • That the item is underpriced.
  • That the greater discount leads to significantly more sales of the item.
  • That the greater discount leads to no significant change in sales of the item.
  • That the item is popular.

Q4. Alpha = 0.05

P-value = 0.08

With the information above, what conclusions can you draw regarding H0 and H1 in a hypothesis test?

  • There is no significant difference. Reject H0, reject H1.
  • There is a significant difference. Reject H0, accept H1.
  • There is no significant difference. Accept H0, reject H1.
  • There is a significant difference. Accept H0, accept H1.

Q5. Repeated measures, AB testing, and randomized control trials are all studies of what type?

  • Bias
  • Observational
  • Experimental
  • Statistics

Q6. What is a statistical assumption?

  • An assumption about the outcome of an analysis.
  • Something that must be true for the analysis to be correct.
  • An assumption about the target audience of the analysis.
  • Something that you know about the data.

Q7. What is the alpha for a 96% confidence interval?

  • 0.96
  • 96%
  • 0.04
  • 0.05

Q8. What are five common types of bias?

  • Survey, Culture, Confirmation, Observation, and Unfair
  • Survey, Culture, Support, Observation, and Selection
  • Survey, Culture, Confirmation, Observation, and Selection
  • Survey, Recording, Confirmation, Experimental, and Selection

Statistics for Marketing Week 04 Quiz Answers

Practice Quiz: Statistical Modeling

Q1. What is statistical analysis?

I. Equations used to analyze data.

II. Field of study based around the use of data.

III. Processes used to analyze data.

  • All three.
  • I.
  • II.
  • III.

Q2. What is statistical modeling?

  • The field of study based around the use of data.
  • The application of a statistical analysis to data.
  • It is the result of analyzing data with a model.
  • The equations or processes used in analyzing data.

Q3. What is a statistical model?

  • The field of study based around the use of data.
  • The results from statistical modeling.
  • The equations used to analyze data.
  • The application of a statistical analysis.

Q4. True or false: Cluster analysis is used to subdivide the data into smaller and more similar groups.

  • True
  • False

Q5. What are the two versions of machine learning?

  • Supervised and unsupervised
  • Regression and classification
  • Regression and clustering
  • Unsupervised and clustering

Q6. What type of machine learning would be suitable for data that is unlabeled?

  • Supervised
  • Unsupervised
  • Regression
  • Categorical

Q7. Regression and Classification models fall under which type of machine learning?

  • Supervised
  • Unsupervised

Practice Quiz: Simple Linear Regression

Q1. In simple linear regression, what does the word “linear” mean?

  • That a line is used to relate the independent and dependent variables.
  • That the independent and dependent variables are not related.
  • That the regression is easy to perform.
  • That there is only one independent variable.

Q2. What does the r2 in a linear regression mean?

  • It tells you how good the model is.
  • It is the slope of the line created by the regression.
  • It is the accuracy of the model.
  • It is a measure of how much of the variance is explained by the independent variable.

Q3. What is the residual?

  • It is the collection of data that was not used to construct the model.
  • It is the set of outliers in the data.
  • It is the set of data values that are close to the regression line.
  • It is the difference between the recorded data value and the predicted data value.

Q4. Which of the following is one of the assumptions in simple linear regression?

I. Linearity

II. Randomness

III. Maximum sample size

  • I.
  • II.
  • III.
  • All of these.

Q6. Regression and classification models are similar in that they both:

  • Predict numerical variables.
  • Predict categorical variables.
  • Use dependent variables to predict independent variables.
  • Use independent variables to predict dependent variables.

Q7. True or false: K-Nearest neighbors is a type of classification model.

  • True
  • False

Practice Quiz: Cluster Analysis

Q1. How is clustering analysis useful to a marketing analyst?

  • It determines what marketing strategy to employ.
  • It homogenizes the sample.
  • It determines if a marketing strategy is effective.
  • It facilitates market segmentation.

Q2. What is market segmentation?

  • It is the use of statistical models on a target population.
  • It is the assumption that sub-groups of the population have the same likelihood of occurring.
  • It is the process of creating sub-groups in a customer base using common traits or needs.
  • It is the belief that markets are diverse.

Q3. What clustering method is often considered the default method?

  • Mean shift.
  • Hierarchical.
  • K-means.
  • Density-based spatial.

Q4. What is the assumption of homogeneity of variance in K-means clustering?

  • That all the variables in the analysis have similar variance.
  • That every cluster has the same likelihood of occurring.
  • That the minimum sample size is 50 times the number of clusters.
  • That the clusters are approximately elliptical, or round.

Practice Quiz: Time Series

Q1. What does a time series analysis do?

  • It evaluates a qualitative variable to see how it changes in time.
  • It tracks how the independent variable changes with time.
  • It evaluates a quantitative variable to see how it changes in time.
  • It shortens the length of time to complete a statistical analysis.

Q2. What is the purpose, or most common use, for time series analysis?

  • It identifies different sub-groups in the data that share similar traits.
  • To predict, or forecast, future values of the dependent variable.
  • It keeps track of how long an analysis takes to complete.
  • It records the past values of the dependent variable.

Q3. Which of the following is NOT an assumption in time series analysis?

I. Dependence

II. Independence

III. Minimum sample size

  • I.
  • II.
  • All three.
  • III.

Q4. True or false: The minimum sample size in time series analysis is 50.

  • False
  • True

Graded Quiz: Statistical Modeling

Q1. Consider the assumptions in simple linear regression. What is the assumption of minimum sample size?

  • The minimum sample is 100.
  • The minimum sample is 10.
  • The minimum sample is 50.
  • The minimum sample is 20.

Q2. Which one of the following is a modeling technique in clustering analysis?

I. Logistic regression

II. Interpolation

III. Naive Bayes.

  • I.
  • III.
  • II.
  • All of these.

Q3. In time series analysis, the assumption that the data values all come from the same source is known as:

  • dependence.
  • stationarity.
  • minimum sample size.
  • constant time.

Q4. In time series analysis, you can convert longer units of time into shorter units of time.

  • False.
  • True.

Q5. What is the difference between a classification model and a regression model?

  • A classification model is used with numerical data, while a regression model works with qualitative variables.
  • A classification model is used with qualitative data, while a regression model works with categorical data.
  • A classification model predicts a quantitative variable, while a regression model predicts a qualitative variable.
  • A classification model predicts a qualitative variable, while a regression model predicts a quantitative variable.

Q6. What is the assumption of minimum sample size for K-means clustering?

  • 10 times the number of clusters.
  • 100 times the number of clusters.
  • 50 times the number of clusters.
  • 20 times the number of clusters.

Q7. In time series, what is the assumption of stationarity?

  • That the data follows a normal distribution.
  • That the mean value of the series is constant.
  • That the data values come from the same source.
  • That the minimum sample size is 50.

Q8. In time series analysis, what is the assumed minimum sample size if time is measured in quarters?

  • 40 quarters of information.
  • 100 quarters of information.
  • 50 quarters of information.
  • 4 quarters of information.

Q9. Which of the following is NOT an assumption in simple linear regression?

I. Independence

II. Homogeneity of variance

III. Sphericity

  • II.
  • III.
  • I.
  • All of them are.

Q10. Which of the following is a type of quantitative variable?

  • Dependent.
  • Categorical.
  • Independent.
  • Continuous.
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