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210 Statistics Practice Exam

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1. Descriptive Statistics • Data Types and Scales o Understand different data types: nominal, ordinal, interval, and ratio. o Recognize scales of measurement and their implications for data analysis. • Data Summarization o Calculate and interpret measures of central tendency: mean, median, mode. o Compute and understand measures of variability: range, variance, standard deviation, interquartile range. • Data Visualization o Construct and interpret various plots: histograms, box plots, bar charts, pie charts, scatter plots. o Understand the purpose and construction of stem-and-leaf displays. 2. Probability Theory • Basic Probability Concepts o Define probability, sample space, and events. o Apply the addition and multiplication rules of probability. o Understand conditional probability and independence. • Random Variables and Probability Distributions o Differentiate between discrete and continuous random variables. o Calculate expected value and variance for discrete random variables. o Understand and apply binomial and Poisson distributions. o Identify and use the normal distribution, including standardization and z-scores. 3. Sampling Distributions • Concept of Sampling Distribution o Understand the concept of a sampling distribution and its significance. o Apply the Central Limit Theorem. • Estimation o Construct and interpret point estimates and confidence intervals for population parameters (mean, proportion, variance). o Determine sample sizes for estimating population parameters with desired precision. 4. Statistical Inference • Hypothesis Testing o Formulate null and alternative hypotheses. o Conduct hypothesis tests for population means and proportions (z-tests, t-tests). o Understand p-values, significance levels, and Type I/Type II errors. o Perform chi-square tests for categorical data. • Analysis of Variance (ANOVA) o Conduct one-way ANOVA to compare means across multiple groups. o Understand assumptions of ANOVA and interpret results. 5. Regression and Correlation • Simple Linear Regression o Understand the least squares method for fitting a regression line. o Interpret regression coefficients and assess model fit (R-squared, residual analysis). • Multiple Regression o Extend simple linear regression to multiple predictors. o Interpret coefficients, assess multicollinearity, and perform model selection. • Correlation Analysis o Calculate and interpret Pearson's correlation coefficient. o Distinguish between correlation and causation. 6. Non-Parametric Methods • Chi-Square Tests o Conduct chi-square tests for independence and goodness-of-fit. • Non-Parametric Tests o Apply tests like the Wilcoxon signed-rank test and Kruskal-Wallis test when data do not meet parametric assumptions. 7. Statistical Software and Data Analysis • Data Management o Import, clean, and organize data using statistical software (e.g., R, SPSS, SAS). • Statistical Analysis o Perform descriptive and inferential statistical analyses. • Interpretation and Communication o Interpret statistical outputs and effectively communicate findings. 8. Applied Statistics in Social Sciences • Survey Sampling o Understand sampling methods and design surveys for data collection. • Experimental Design o Design and analyze experiments, considering control groups and randomization. • Ethical Considerations o Address ethical issues related to data collection, analysis, and reporting.

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210 Statistics Practice Exam
Question 1: Which of the following is an example of nominal data?
A) Age groups (20–29, 30–39)
B) Blood pressure readings
C) Types of fruits
D) Temperature in Celsius
Answer: C
Explanation: Nominal data classify items into distinct categories without any inherent order, such as
types of fruits.

Question 2: Which data type is characterized by a natural order but not equal intervals between
categories?
A) Nominal
B) Ordinal
C) Interval
D) Ratio
Answer: B
Explanation: Ordinal data have a ranking or order among categories, but the differences between ranks
are not necessarily equal.

Question 3: Which of the following scales of measurement does not possess a true zero?
A) Nominal
B) Ordinal
C) Interval
D) Ratio
Answer: C
Explanation: Interval scales have equal intervals between values but no true zero point, as in the case of
temperature measured in Celsius or Fahrenheit.

Question 4: What distinguishes ratio data from interval data?
A) Ratio data are categorical
B) Ratio data have a true zero point
C) Ratio data lack equal intervals
D) Ratio data are only used for ranking
Answer: B
Explanation: Ratio data have all the properties of interval data, with the added characteristic of a true
zero, which allows for the expression of relative magnitude.

Question 5: Which measure of central tendency is most appropriate for a highly skewed distribution?
A) Mean
B) Median
C) Mode
D) Range
Answer: B

,Explanation: The median is less affected by extreme values and is a better measure of central tendency
in skewed distributions.

Question 6: Which measure is most sensitive to outliers in a data set?
A) Median
B) Mode
C) Mean
D) Interquartile Range
Answer: C
Explanation: The mean is influenced by extreme values, making it sensitive to outliers.

Question 7: In which situation is the mode the best measure of central tendency?
A) Data with a symmetrical distribution
B) Data that are nominal
C) Data with outliers
D) Data measured on an interval scale
Answer: B
Explanation: For nominal data, where the data represent categories, the mode is the most appropriate
measure since it identifies the most frequent category.

Question 8: What does the mode of a dataset represent?
A) The arithmetic average
B) The middle value
C) The most frequently occurring value
D) The spread of the data
Answer: C
Explanation: The mode is defined as the value that appears most often in the dataset.

Question 9: Which statistic is used to describe the variability of a dataset?
A) Mean
B) Median
C) Variance
D) Mode
Answer: C
Explanation: Variance quantifies the spread of the data points around the mean.

Question 10: When would the standard deviation be preferred over the range?
A) When the data set is small
B) When there are outliers present
C) When a more precise measure of variability is needed
D) When data are nominal
Answer: C
Explanation: Standard deviation provides a more precise measure of variability as it takes into account
every data point relative to the mean.

Question 11: How is the range of a data set defined?
A) The sum of all values

,B) The difference between the highest and lowest values
C) The average of the values
D) The most frequent value
Answer: B
Explanation: The range is calculated by subtracting the minimum value from the maximum value in the
dataset.

Question 12: What does the interquartile range (IQR) measure?
A) The difference between the maximum and minimum values
B) The average of the top and bottom quartiles
C) The spread of the middle 50% of the data
D) The variability of the entire data set
Answer: C
Explanation: IQR measures the range within which the central 50% of the values lie, reducing the impact
of outliers.

Question 13: Which graphical tool is best for showing the distribution shape of a continuous variable?
A) Bar chart
B) Histogram
C) Pie chart
D) Pareto chart
Answer: B
Explanation: A histogram displays the frequency distribution of a continuous variable, making it ideal for
observing the shape of the data distribution.

Question 14: What feature of a box plot is particularly useful for identifying outliers?
A) The median line
B) The whiskers
C) The bars
D) The pie slices
Answer: B
Explanation: In a box plot, the whiskers extend to a specified range, and points outside this range are
considered outliers.

Question 15: Which of the following is the most appropriate for summarizing categorical data?
A) Histogram
B) Box plot
C) Bar chart
D) Scatter plot
Answer: C
Explanation: Bar charts are used to display the frequency or proportion of categories in a dataset.

Question 16: Which graph is most effective for showing parts of a whole?
A) Box plot
B) Scatter plot
C) Pie chart
D) Histogram

, Answer: C
Explanation: Pie charts visually represent the proportion of each category as parts of a whole.

Question 17: Which type of plot is ideal for exploring the relationship between two quantitative
variables?
A) Bar chart
B) Pie chart
C) Histogram
D) Scatter plot
Answer: D
Explanation: A scatter plot displays the relationship between two numerical variables by plotting data
points on a two-dimensional graph.

Question 18: What is the primary purpose of a stem-and-leaf display?
A) To compare categorical data
B) To show the frequency distribution and retain original data values
C) To depict the mean and median
D) To calculate the standard deviation
Answer: B
Explanation: A stem-and-leaf plot organizes data while preserving the original values, making it useful
for quickly identifying the distribution shape.

Question 19: Why is the mean considered sensitive to extreme values?
A) It is based on the middle value
B) It only considers the most frequent observation
C) It involves summing all observations, so extreme values can disproportionately affect the total
D) It measures variability
Answer: C
Explanation: Because the mean is the sum of all observations divided by the number of observations,
unusually high or low values can significantly influence it.

Question 20: Which measure of central tendency is known for its robustness in the presence of
outliers?
A) Mean
B) Mode
C) Median
D) Variance
Answer: C
Explanation: The median is less affected by extreme values and therefore provides a better central
location for skewed data.

Question 21: For ordinal data, which measure of central tendency is most informative?
A) Mean
B) Median
C) Mode
D) Range
Answer: B

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