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 have a meaningful order, but differences between values are not meaningful (e.g., rankings, satisfaction levels).

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

• Ratio: Like interval, but with a true zero, allowing for ratios (e.g., height, weight, age).

Example: Measuring temperature in Kelvin (ratio), Celsius (interval), ranking runners (ordinal), or recording eye color (nominal).

Sampling Methods

Types of Sampling

Sampling methods determine how data are collected from a population. Common methods include:

• 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 (systematic), or selecting random students from each grade (stratified).

Types of Data

Discrete vs. Continuous Data

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

• Continuous Data: Can take any value within a range (e.g., height, weight).

Example: Number of cars (discrete), time taken to run a race (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 midpoints or boundaries for the horizontal axis.

• No gaps between bars (unlike bar graphs for categorical data).

• Label axes clearly 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 most frequently occurring value.

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

• Range: 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 values near the center.

Example: Income data are often skewed right; test scores may be bell-shaped.

Z-Scores and Percentiles

Calculating Z-Scores

• Z-Score: Measures how many standard deviations a data value is from the mean.

Formula:

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

• Where x is the data value, μ is the mean, and σ is the standard deviation.

• 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.

Probability

Basic Probability Concepts

• Simple Probability: Probability of a single event occurring.

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

Addition Rule ("OR" Probability)

• For mutually exclusive events: $P(A \text{ or } B) = P(A) + P(B)$

• For non-mutually exclusive events: $P(A \text{ or } B) = P(A) + P(B) - P(A \text{ and } B)$

Multiplication Rule ("AND" Probability)

• For independent events: $P(A \text{ and } B) = P(A) \times P(B)$

• For dependent events, adjust probabilities as needed.

Probability of "At Least One"

• Use the complement rule: $P(\text{at least one}) = 1 - P(\text{none})$

Example: Probability of getting at least one head in three coin tosses: $1 - (1/8) = 7/8$

Best Practices for Calculations

• Show all values used in formulas for mean, standard deviation, probability, z-score, and percentiles.

• It is not necessary to show every arithmetic step, but clarity in the process is important.