Statistics for Data Science: Learn via 700+ MCQs Quiz [2023]
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Statistics for Data Science: Learn via 700+ MCQs Quiz - Updated on July 2023
Master the vital skill of Statistics for Data Science through our comprehensive quiz-based course. Engage, learn, and test your knowledge with 700+ Statistics for Data Science Multiple Choice Questions.
In the fast-paced world of data science, mastering statistics is crucial. Our course, Statistics for Data Science: Learn via 700+ MCQs Quiz, has been meticulously designed to equip you with the essential statistical knowledge required to thrive in the data science landscape.
This course isn't your typical lecture-style class. Instead, we've developed a unique, interactive format focused on learning through multiple-choice questions. We believe the best way to understand and internalize "statistics for data science" is by continually testing and applying your knowledge. And what better way to do so than via a vast repository of 700+ MCQs?
What You Will Learn:
Section 1: Descriptive Statistics
Introduction to Statistics
Types of Data: Quantitative vs. Qualitative
Measures of Central Tendency (Mean, Median, Mode)
Measures of Dispersion (Range, Variance, Standard Deviation)
Measures of Shape (Skewness and Kurtosis)
Understanding Distributions (Uniform, Normal, Skewed)
Data Visualization: Box plots, Histograms, and Bar Plots
Section 2: Probability Theory
Basics of Probability (Experiments, Outcomes, Events)
Rules of Probability (Addition and Multiplication Rules)
Conditional Probability and Independence
Bayes' Theorem
Random Variables and Probability Distributions (Discrete and Continuous)
Special Distributions (Uniform, Binomial, Normal, Poisson)
Central Limit Theorem and Law of Large Numbers
Section 3: Inferential Statistics
Sampling and Sampling Distributions
Point and Interval Estimation
Confidence Intervals for Mean and Proportions
Hypothesis Testing Basics (Null and Alternative Hypotheses)
Z-tests and T-tests for Means
Chi-square Tests for Independence
Understanding Errors in Hypothesis Testing (Type I and Type II Errors, Power of a Test)
Section 4: Correlation and Regression
Scatter Plots and Correlation
Pearson's Correlation Coefficient
Simple Linear Regression (Assumptions, Estimation, Inference)
Residual Analysis and Diagnostics in Simple Linear Regression
Multiple Linear Regression
Inference in Multiple Linear Regression
Multicollinearity and Model Selection in Multiple Regression
Section 5: Multivariate Analysis
Extensions of Regression Analysis (Polynomial Regression, Interaction Effects)
Introduction to Analysis of Variance (ANOVA)
One-way and Two-way ANOVA
Principal Component Analysis (PCA)
Factor Analysis
Cluster Analysis
Section 6: Non-parametric Tests
Introduction to Non-parametric Statistics
Sign Test and Wilcoxon Signed Rank Test
Mann-Whitney U Test
Kruskal-Wallis Test
Spearman’s Rank Correlation
Chi-square Test for Goodness of Fit
Here are some example MCQs for the sections mentioned.
Section 1: Descriptive Statistics
1. Introduction to Statistics
Q1: In the context of statistics for data science, which of the following best describes the purpose of statistics?
A. Only to gather data
B. Only to present data visually
C. To make predictions about future trends
D. To make the computer run faster
Correct Option: C.
Explanation: In "statistics for data science", the main goal of statistics is not merely to gather or present data but to analyze it in order to make informed decisions, predictions about future trends, and interpret complex data sets.
2. Types of Data: Quantitative vs. Qualitative
Q2: Which type of data is best suited for a pie chart visualization in statistics for data science?
A. Continuous Quantitative Data
B. Discrete Quantitative Data
C. Nominal Qualitative Data
D. Ordinal Quantitative Data
Correct Option: C.
Explanation: In statistics for data science, nominal qualitative data, which refers to non-numerical data that can be categorized, is often best visualized using a pie chart. The pie chart clearly shows the proportion of each category in the total.
3. Measures of Central Tendency (Mean, Median, Mode)
Q3: Which measure of central tendency is not affected by outliers in statistics for data science?
A. Mean
B. Mode
C. Median
D. All are affected by outliers
Correct Option: C.
Explanation: In statistics for data science, the median, which is the middle value of a data set when ordered, is not affected by extreme values (outliers), unlike the mean. The mode, representing the most common value in a data set, can potentially be influenced by outliers if they occur frequently.
4. Measures of Dispersion (Range, Variance, Standard Deviation)
Q4: Which measure of dispersion is most affected by outliers in statistics for data science?
A. Range
B. Variance
C. Standard Deviation
D. Coefficient of Variation
Correct Option: A.
Explanation: In "statistics for data science", the range, which is calculated as the difference between the largest and the smallest data point in the dataset, is most affected by outliers as it only considers these two points and not the overall data distribution.
5. Measures of Shape (Skewness and Kurtosis)
Q5: In statistics for data science, a distribution is considered "positively skewed" if...?
A. The tail is longer on the left side
B. The tail is longer on the right side
C. It is a normal distribution
D. The distribution has no tail
Correct Option: B.
Explanation: In "statistics for data science", a distribution is said to be positively skewed if the tail on the right side (larger end of the distribution) is longer. This means that a few data points are significantly larger than the rest.
6. Understanding Distributions (Uniform, Normal, Skewed)
Q6: Which of the following distributions has a bell-shaped curve in statistics for data science?
A. Uniform Distribution
B. Skewed Distribution
C. Normal Distribution
D. None of the above
Correct Option: C.
Explanation: In statistics for data science, a normal distribution, also known as a Gaussian distribution, has a bell-shaped curve. It is symmetrical around the mean, indicating that data near the mean are more frequent in occurrence than data far from the mean.
7. Data Visualization: Box plots, Histograms, and Bar Plots
Q7: In statistics for data science, which of the following visualizations can be used to identify outliers?
A. Bar plot
B. Pie chart
C. Line graph
D. Box plot
Correct Option: D.
Explanation: In "statistics for data science", box plots are an excellent tool for identifying outliers. They represent the interquartile range (where most of the data lie) and any data point outside this range (represented by 'whiskers') is considered an outlier.
Section 2: Probability Theory
1. Basics of Probability (Experiments, Outcomes, Events)
Q1: In statistics for data science, what does an 'event' refer to in the context of probability?
A. An experiment
B. An outcome
C. A set of outcomes
D. None of the above
Correct Option: C.
Explanation: In statistics for data science, an 'event' in the context of probability refers to a set of outcomes from the sample space. An event may consist of one outcome, multiple outcomes, or even no outcome.
2. Rules of Probability (Addition and Multiplication Rules)
Q2: In statistics for data science, when is the addition rule of probability used?
A. To calculate the probability of the intersection of two events
B. To calculate the probability of the union of two events
C. To calculate the conditional probability of an event
D. To calculate the inverse probability of an event
Correct Option: B.
Explanation: In statistics for data science, the addition rule of probability is used to calculate the probability of the union of two events (i.e., the probability that either of the two events happens).
3. Conditional Probability and Independence
Q3: In statistics for data science, if two events are independent, the probability of both occurring is given by...?
A. The sum of their individual probabilities
B. The difference of their individual probabilities
C. The product of their individual probabilities
D. None of the above
Correct Option: C.
Explanation: In statistics for data science, if two events are independent, then the probability of both events occurring is the product of the probabilities of each event.
4. Bayes' Theorem
Q4: In statistics for data science, Bayes' Theorem is often used to...?
A. Calculate the mean of a dataset
B. Predict future events
C. Update prior probabilities given new data
D. Establish causality between variables
Correct Option: C.
Explanation: In statistics for data science, Bayes' theorem is often used to update prior probabilities given new data. This theorem forms the basis of Bayesian inference, where the probability of a hypothesis is updated as more evidence or information becomes available.
5. Random Variables and Probability Distributions (Discrete and Continuous)
Q5: In statistics for data science, which of the following can be represented by a continuous random variable?
A. The number of students in a class
B. The roll of a die
C. The height of a person
D. The number of tails in 3 coin flips
Correct Option: C.
Explanation: In statistics for data science, the height of a person can be represented by a continuous random variable, as it can take on any value within a specified range and is not just limited to distinct separate values.
6. Special Distributions (Uniform, Binomial, Normal, Poisson)
Q6: In statistics for data science, which distribution would be most appropriate to model the number of emails arriving in your inbox in a given hour?
A. Uniform Distribution
B. Binomial Distribution
C. Normal Distribution
D. Poisson Distribution
Correct Option: D.
Explanation: In statistics for data science, the Poisson distribution would be most appropriate to model the number of emails arriving in your inbox in a given hour. The Poisson distribution models the number of events (in this case, emails) occurring in a fixed interval of time.
7. Central Limit Theorem and Law of Large Numbers
Q7: In statistics for data science, the Central Limit Theorem is important because it...?
A. Ensures that data is normally distributed
B. Allows us to use normal distribution approximations for large datasets
C. States that the sum of a number of random variables behaves like a normal distribution
D. All of the above
Correct Option: D.
Explanation: In statistics for data science, the Central Limit Theorem is important for all of the reasons listed above. It states that the sum or average of a large number of independent and identically distributed random variables, no matter the original distribution, approaches a normal distribution. This allows us to make predictions about large datasets using the normal distribution approximation.
Section 3: Inferential Statistics
1. Sampling and Sampling Distributions
Q1: In statistics for data science, which sampling method ensures every member of the population has an equal chance of being selected?
A. Stratified Sampling
B. Cluster Sampling
C. Simple Random Sampling
D. Convenience Sampling
Correct Option: C.
Explanation: In statistics for data science, Simple Random Sampling ensures that every member of the population has an equal chance of being selected. This type of sampling is akin to a random lottery draw where each ticket (i.e., each population member) has an equal chance of being drawn.
2. Point and Interval Estimation
Q2: In statistics for data science, what does a confidence interval estimate?
A. The precise value of the population parameter
B. The likely range of values of the population parameter
C. The variance of the population
D. The size of the sample needed for a study
Correct Option: B.
Explanation: In statistics for data science, a confidence interval provides an estimated range of values which is likely to include an unknown population parameter. The width of the confidence interval gives us an idea about how uncertain we are about the unknown parameter.
3. Confidence Intervals for Mean and Proportions
Q3: In statistics for data science, if we want to increase the confidence level of an interval estimate, what happens to the width of the confidence interval