Math 205 FG                Review List for Test 1               Dr. Fenton

  CONCEPTS TECHNIQUES
SAMPLE PROBLEMS

Chapter 1:
Looking at Data

 

Types of variables:
categorical or quantitative

Displaying distributions by graphs

Describing distributions: center, spread, skew, symmetry, outliers

Outliers and gaps

Resistant statistics

Normal distribution:
68-95-99% rule

Making graphs:

  • categorical variable:
    bar graphs, pie charts
  • quantitative variable: stemplots, histograms, boxplots, time plots

Calculating median and mean

Calculating: range, quartiles, standard deviation

Calculating z-scores

Using normalcdf
  • 1.10
  • 1.17
  • 1.20
  • 1.21
  • 1.26
  • 1.28
  • 1.56
  • 1.60
  • 1.63(a,c)
  • 1.65
  • 1.73
  • 1.82
  • 1.112
  • 1.114
  • 1.117
  • 1.121
  • 1.126
  • 1.128
  • 1.137
  • 1.159
  • 1.160

Chapter 2: Relationships between Data

 

Explanatory variable and response variable

Positive association, negative association

Strength of a linear relationship, as measured by r

Interpolation versus extrapolation

Meaning of r squared

Influential observations versus outliers

Comparing a categorical variable to a quantitative variable by parallel boxplots

Causation: cause & effect, common response, confounding variables.

Making scatterplots

Calculating the correlation coefficient

Calculating the regression line and adding it to the scatterplot

Two-way tables:

  • Finding relative frequencies
  • Calculating conditional distributions
  • Calculating the marginal distribution
  • Making bar graphs and pie charts
Drawing diagrams for possible causation situations
  • 2.9
  • 2.11
  • 2.15
  • 2.19
  • 2.22
  • 2.35
  • 2.39
  • 2.41(a,b)
  • 2.52
  • 2.62
  • 2.68
  • 2.87(a,b)
  • 2.95
  • 2.114
  • 2.115
  • 2.120
  • 2.122
  • 2.123
  • 2.124
  • 2.138
  • 2.147
  • 2.156
Chapter 3: Producing Data

Weakness of anecdotal evidence

Basic principles of experimental design: Compare, Randomize, Repeat

Population versus sample

Potential problems of sampling: undercoverage, nonresponse, response bias

Sampling distribution of a statistic

Bias and variability for a sampling distribution

Designing an experiment: control group, experimental group, random allocation

Diagramming an experimental design

Designing a sampling procedure:

  • Simple random sample
  • Stratified random sample
  • Multistage random sample

Using random numbers
  • 3.15
  • 3.26
  • 3.29(a)
  • 3.54
  • 3.68
  • 3.84
  • 3.88
  • 3.115
  • 3.123(a,b)
  • 3.125

The emphasis will be on understanding the concepts and the techniques. You should be able to use your calculator to create graphs and calculate values but, more importantly, you should be able to interpret the results.