Modular 1

1. Modular 1

2. Introduction of the Course Structure and MyLabsPlus

3. Ch 1.1 & 1.2 Basic Definitions for Statistics Objective A : Basic Definition Objective B : Level of measurement of a Variable Objective C : Observational Study versus Designed Experiment

4. Ch 1.1 & 1.2 Basic Definitions for Statistics Objective A : Basic Definition A1. Definition • What is Statistics? • Statistics is the science of collecting, organizing, summarizing, and analyzing data to draw conclusions. • Descriptive Statistics • Statistics • Inferential Statistics • Descriptive statistics consist of collecting, organizing, summarizing, and presenting data. • Inferential statistics consists of generalizing from samples to populations, performing estimations and hypothesis tests, and making predictions.

5. Population versus Sample • A population consists of all individuals (person or object) that are being studied. • A sample is a subset of the population. • Parameter versus Statistic • A parameter is a numerical summary of a population. • A statistic is a numerical summary of a sample.

6. Example 1: • Identify the population and sample in the study. A farmer wanted to learn about the weight of his soybean crop. He randomly sampled 100 plants and weighed the soybeans on each plant. • All soybean plants planted by the farmer. • Population – • Sample – • 100 randomly selected soybean plants. • Example 2: • Determine whether the underlined value is a parameter or a statistic. • (a) Only 12 men have walked on the moon. The average age of these men at the time of their moonwalks was 39 years, 11 months, 15 days. • Parameter. (b) In a national survey on substance abuse, 66.4% of respondents who were full-time college students aged 18 to 22 reported using alcohol within the past month. • Statistic.

7. A2. Variables and Type of Data • Variable • A variable is a characteristic that can assume different values called data. • Qualitative versus Quantitative Variable • Qualitative (categorical) variables are variables that can be placed into distinct categories. • Quantitative (numerical) variables are numerical and arithmetic operations can be performed. • Discrete variable • Quantitative variable • Continuous variable • A discrete variable can assume a countable number of values. • A continuous variable can assume an infinite number of values between any two specific values. They often include fractions and decimals.

8. Example 1: • Classify each variable as qualitative or quantitative. If the variable is quantitative, further classify the data as discrete or continuous. • (a) Number of students attending a university for Fall 2012. • Quantitative • Discrete (b) Colors of football caps in a store. • Qualitative (c) Social security number. • Qualitative (d) Water temperature of a swimming pool. • Quantitative • Continuous

9. Ch 1.1 & 1.2 Basic Definitions for Statistics Objective A : Basic Definition Objective B : Level of measurement of a Variable Objective C : Observational Study versus Designed Experiment

10. Ch 1.1 & 1.2 Basic Definitions for Statistics Objective B: Level of measurement of a Variable In addition to being classified as qualitative or quantitative, variables can be classified by how they are categorized, counted, or measured. Four common types of measurement scales are used: nominal, ordinal, interval, and ratio. • The nominal levelof measurement classifies data into categories in which no order or ranking can be imposed on the data. For example: Eye color • The ordinal levelof measurement classifies data into categories that can be ranked. For example: Course grade

11. The interval level of measurement ranks data and differences in the values of the variable have meaning. Also there is no true zero. For example: Sea level; Temperature • The ratio level of measurement ranks data, differences in the values or the variable have meaning, and there exists a true zero. For example: Length

12. Ch 1.1 & 1.2 Basic Definitions for Statistics Objective A : Basic Definition Objective B : Level of measurement of a Variable Objective C : Observational Study versus Designed Experiment

13. Ch 1.1 & 1.2 Basic Definitions for Statistics Objective C: Observational Study versus DesignedExperiment Definition • In an observational study, the researcher observes the behavior of the individuals without trying to influence the outcome of the study. • In a designed experiment, the researcher controls one of the variables and tries to determine how the manipulation influences other variables. • The independent variable which is also called the explanatory variable in a designed experiment is the one that is being controlled by the researcher. The dependent variable which is also called the response variable is the resultant variable.

14. Confounding in a study occurs when the effects of two or more explanatory variables are not separated. • A lurking variable is an explanatory variable that was not considered in a study, but that effects the value of the response variable in the study.

15. Example 1: • Determine whether the study depicts an observational study or an experiment. • (a) Rats with cancer are divided into two groups. One group receives 5 mg of a medication that is thought to fight cancer, and the other receives 10 mg. After 2 years, the spread of the cancer is measured. • An experiment (b) Conservation agents netted 320 large-trout in a lake and determined how many were carrying parasites. • An observational study

16. Example 2: • Identify the explanatory variable and the response variable for the following studies. • (a) Rats with cancer are divided into two groups. One group receives 5 mg of a medication that is thought to fight cancer, and the other receives 10 mg. After 2 years, the spread of the cancer is measured. • the amount of medication dosage • Explanatory variable – • measure of the spread of the cancer • Response variable – (b) A researcher wants to determine whether young couples who marry are more likely to gain weight than those who stay single. • marital status • Explanatory variable – • Response variable – • weight gained