**Descriptive and Inferential Statistics Applications**

Introduction

In Statistics we come across two main categories: Descriptive and Inferential Statistics.

Descriptive: Statistics that merely describe the group they belong to.

Inferential: Statistics that are used to draw conclusions about a larger group of people.

We will examine each one in more detail and provide examples.

Inferential Statistics

Inferential Statistics is concerned with making predictions or inferences about a population from observations and analyses of a sample. It is imperative that the sample is representative of the group to which it is being generalized.

Helpful in reaching conclusions that extend beyond the immediate data alone.

They are useful when attempting to infer from the sample data what the population might think.

Microsoft® Excel® Functions

Descriptive Statistic Excel Functions

Inferential Statistics Excel Functions

Example 1

According to our recent poll, 43% of Americans brush their teeth incorrectly.

Example 2

Our research indicates that only 33% of people eat breakfast

Example 1

The class did well on its first exam, with a mean (average) score of 89.5% and a standard deviation of 7.8%.

Example 2

This season, the Brawley High School Soccer Team scored a mean (average) of 2.3 goals per game.

Descriptive Statistics

Descriptive statistics is the term given to the analysis of data that helps describe, show or summarize data in a meaningful way such that, for example, patterns might emerge from the data.

They provide simple summaries about the sample and the measures.

These very useful statistics bring together large amounts of data so they can be presented and comprehended with minimal effort.

**Jessica Delgado & Rossy Sanchez**

September 2, 2014

BSHS 435

Beatriz Zayas

September 2, 2014

BSHS 435

Beatriz Zayas

Agenda

Introduction

Variables & Level of Measurement Classification (nominal, ordinal, interval, or ratio).

Descriptive Statistics

Inferential Statistics

Microsoft® Excel® Functions

References

Variables & Level of Measurement Classification

(nominal, ordinal, interval, or ratio)

References

As previously taught variable are an element, feature, or factor that is liable to vary or change with levels of measurement.

Nominal: Gender- Male or Female

Ordinal: Education Experience- Elementary School, High School, Some College, College Graduate

Interval: Annual Income- $10,000, $15,000, $20,000

Ratio: pH levels, enzyme activity, temperature

How Much Water Have You Eaten Today?

Mean 30.3625

Standard Error 11.13259806

Median 9.2

Mode #N/A

Standard Deviation 44.53039224

Sample Variance 1982.955833

Kurtosis 5.715041044

Skewness 2.236594833

Range 167.8

Minimum 0.2

Maximum 168

Sum 485.8

Count 16

56.3

40

115

21.2

56

1.9

0.7

1.6

4.7

13.8

0.2

7.8

38.7

168

82.5

10.6

45.7

1.5

3.8

48.3

Random Sample

13.8

10.6

0.7

38.7

0.2

t-Test: Two-Sample Assuming Equal Variances

Variable 1 Variable 2

Mean 33.04444444 38.26363636

Variance 1472.682778 2522.452545

Observations 9 11

Pooled Variance 2055.888204

Hypothesized Mean Difference 0

df 18

t Stat -0.25609796

P(T<=t) one-tail 0.40038956

t Critical one-tail 1.734063592

P(T<=t) two-tail 0.80077912

t Critical two-tail 2.100922037

Hussain, M. (2012). Descriptive statistics--presenting your results I. JPMA. The Journal Of The Pakistan Medical Association, 62(7), 741-743.

Imperial County Farm Bureau. (2014). Retrieved from http://www.icfb.net/

Omair, A. (2012). Presenting your results-II: Inferential statistics. JPMA. The Journal Of The Pakistan Medical Association, 62(11), 1254-1257.

Orris, J.B. (2014). Basic Statistics Using Excel and MegaStat [University of Phoenix Custom Edition eBook]. : McGraw-Hill Company . Retrieved from University of Phoenix, website.