STATSSS

1. descriptive statistics

2. inductive or inferential statistics

1. descriptive statistics

2. inductive or inferential statistics

What is the two major areas of statistics?

STATISTICS

deals with the methods of collecting, presenting, analyzing and interpreting data so that valid conclusions can be drawn from them.

DESCRIPTIVE STATISTICS

methods concerned with data collection presentation and to the description of some of its features to yield meaningful information without attempting to draw any inferences from it.

INDUCTIVE OR INFERENTIAL STATISTICS 

methods concerned with developing and using mathematical tools to make forecasts and inferences.

POPULATION

the entire group that you want to draw conclusions about.

SAMPLE

the specific group that you will collect data from.

VARIABLE

The characteristics that are being studied. It may be qualitative or quantitative.

COLLECTION OF DATA

• SIMPLE RANDOM SAMPLING

• STRATIFIED SAMPLING

• SYSTEMATIC RANDOM SAMPLING

• CLUSTER SAMPLING

SIMPLE RANDOM SAMPLING

any particular subset of the specified size has the same chance of being selected.

STRATIFIED SAMPLING

A population is divided into subgroups, called strata, and a sample is selected from each stratum.

SYSTEMATIC RANDOM SAMPLING

the items or individuals of the population are arranged in some way - alphabetically, in a file drawer by date received, or by some other method. A random starting point is selected, and then every kth member of the population is selected for the sample.

CLUSTER SAMPLING

is often employed to reduce the cost of sampling a population scattered over a large geographic area.

FREQUENCY DISTRIBUTIONS

The organization of data in tabular form. Data in it may be grouped or ungrouped.

RAW DATA

are collected data that have not been organized numerically.

ARRAY

An arrangement of raw data in ascending or descending order or magnitude

FREQUENCY

The number or times a value appears in the listing

RELATIVE FREQUENCY

any observation is obtained by dividing the actual frequency of the observation by the total frequency.

UNGROUPED DATA

When data is small (n<30) or when there are few distinct values. The data is organized without grouping.

GROUP DATA

Statistical data generated in large masses (n>30) can be assessed by grouping the data into different classes.

RANGE

difference between the largest and smallest value.

CLASSES

represent the grouping or classification.

CLASS INTERVAL

The range of values in a class consisting of a lower limit and an upper limit.

CLASS MARK

The midpoint of the class interval

CLASS BOUNDARIES

A point that represents half way, or a dividing point between successive classes

CUMULATIVE FREQUENCY DISTRIBUTIONS

It is the total frequency of all values either “less than” or “more than” any class boundary.

1. Population Mean (μ)

2. Population Standard Deviation (σ)

3. Population Binomial Proportion (p)

3 PARAMETERS OF INTEREST

ESTIMATION

Estimating or predicting the value of the parameter.

HYPOTHESIS TESTING

Making a decision about the value of a parameter based on some preconceived idea about what its value might be.

• Point Estimation

• Interval Estimation

TYPES OF ESTIMATORS

POINT ESTIMATION

Based on sample data, a single number is calculated to estimate the population parameter. The resulting number is called the point estimate.

INTERVAL ESTIMATION

Based on sample data, two numbers are calculated to form an interval within which the parameter is expected to lie. The resulting pair of numbers is called an interval estimate or confidence interval.