Introductory Statistics Exam 1 Study Guide

Levels of Measurement

Types of Measurement Scales

Understanding the level of measurement is essential for selecting appropriate statistical methods. The four main levels are:

• Nominal: Data are categorized without a natural order or ranking (e.g., gender, colors).

• Ordinal: Data are categorized with a meaningful order, but differences between values are not meaningful (e.g., class rankings, satisfaction ratings).

• Interval: Ordered data with meaningful differences, but no true zero point (e.g., temperature in Celsius).

• Ratio: Ordered data with meaningful differences and a true zero point (e.g., height, weight, age).

Example: Temperature in Fahrenheit is interval; number of students in a class is ratio.

Sampling Methods

Types of Sampling

Sampling methods determine how data are collected from a population:

• Random Sampling: Every member of the population has an equal chance of being selected.

• Stratified Sampling: The population is divided into subgroups (strata) and random samples are taken from each stratum.

• Cluster Sampling: The population is divided into clusters, some clusters are randomly selected, and all members of chosen clusters are sampled.

• Systematic Sampling: Every k-th member of the population is selected after a random starting point.

• Convenience Sampling: Samples are taken from a group that is easy to access, but may not be representative.

Example: Surveying every 10th person entering a store is systematic sampling.

Types of Data

Discrete vs. Continuous Data

• Discrete Data: Countable values (e.g., number of cars, number of students).

• Continuous Data: Measurable values that can take any value within a range (e.g., height, weight, time).

Example: The number of books is discrete; the weight of a book is continuous.

Frequency Distributions

Key Components

• Class Boundaries: The values that separate classes without gaps.

• Class Midpoints: The average of the upper and lower class limits.

• Class Width: The difference between consecutive lower class limits.

Constructing Frequency Tables

• Relative Frequency Distribution: Shows the proportion of data in each class.

• Cumulative Frequency Distribution: Shows the total number of data values less than or equal to the upper class boundary.

Example Table: Relative Frequency Distribution

Class Interval	Frequency	Relative Frequency
10-19	5	0.10
20-29	12	0.24
30-39	18	0.36
40-49	15	0.30

Additional info: Relative frequency is calculated as frequency divided by total number of observations.

Frequency Histograms

Constructing a Histogram

• Use class boundaries or midpoints for the horizontal axis.

• No gaps between bars (for continuous data).

• Label axes and start the vertical axis at 0.

Example: A histogram showing the distribution of test scores in intervals of 10 points.

Measures of Central Tendency and Position

Calculating the Mean

• For grouped data, use the formula:

$\bar{x} = \frac{\sum (f \cdot x)}{\sum f}$

• Where f is the frequency and x is the class midpoint.

Other Measures

• Median: The middle value when data are ordered.

• Mode: The value(s) that occur most frequently.

• Midrange: The average of the highest and lowest values.

• Range: The difference between the highest and lowest values.

• Percentile: The value below which a given percentage of data falls.

Example: In the data set {2, 4, 4, 7, 9}, the mode is 4, the median is 4, and the range is 7.

Measures of Variation

Variance and Standard Deviation

• Variance (s2): The average of the squared differences from the mean.

• Standard Deviation (s): The square root of the variance.

Formula for standard deviation (sample):

$s = \sqrt{ \frac{ n (\sum x^2) - (\sum x)^2 }{ n(n-1) } }$

To find variance from standard deviation: $s^2$

Shape of Distributions

Types of Distribution Shapes

• Skewed Left: Tail on the left side is longer; mean < median.

• Skewed Right: Tail on the right side is longer; mean > median.

• Uniform: All values are equally likely.

• Bell-Shaped (Normal): Symmetrical, with most data near the center.

Example: Test scores often form a bell-shaped distribution.

Z-Scores and Percentiles

Calculating Z-Scores

• A z-score measures how many standard deviations a data value is from the mean.

Formula:

$z = \frac{ x - \mu }{ \sigma }$

• For sample data, use $\bar{x}$ and $s$ instead of $\mu$ and $\sigma$.

• To find a data value from a z-score: $x = \mu + z \sigma$

• Values with z-scores less than -2 or greater than 2 are often considered significantly low or high.

Example: If a test score is 85, the mean is 75, and the standard deviation is 5, then $z = (85-75)/5 = 2$.

Probability

Basic Probability Concepts

• Simple Probability: $P(A) = \frac{\text{Number of favorable outcomes}}{\text{Total number of outcomes}}$

• Addition Rule ("OR"): $P(A \text{ or } B) = P(A) + P(B) - P(A \text{ and } B)$

• Multiplication Rule ("AND"): $P(A \text{ and } B) = P(A) \times P(B|A)$

• For independent events, $P(B|A) = P(B)$; for dependent events, adjust probabilities as needed.

• Probability of "At Least One": $P(\text{at least one}) = 1 - P(\text{none})$

Example: The probability of drawing at least one ace in two draws from a deck (without replacement) can be found using the complement rule.

Summary Table: Key Formulas

Concept	Formula
Mean (grouped data)	$\bar{x} = \frac{\sum (f \cdot x)}{\sum f}$
Standard Deviation (sample)	$s = \sqrt{ \frac{ n (\sum x^2) - (\sum x)^2 }{ n(n-1) } }$
Z-score	$z = \frac{ x - \mu }{ \sigma }$
Simple Probability	$P(A) = \frac{\text{Number of favorable outcomes}}{\text{Total number of outcomes}}$
Addition Rule	$P(A \text{ or } B) = P(A) + P(B) - P(A \text{ and } B)$
Multiplication Rule	$P(A \text{ and } B) = P(A) \times P(B|A)$
At Least One	$P(\text{at least one}) = 1 - P(\text{none})$

Additional info: Showing work for calculations is important for full credit on exams. Always write out the formula and substitute the values before computing the final answer.