Week one Introduction to Statistics Chs 221 Dr

Week one Introduction to Statistics Chs 221 Dr. wajed Hatamleh Chapter 1 Population Sample Variable and parameter Variables Post test Dr. Wajed Hatamleh Slide 1

Faculty Information • • • https: //staff. ksu. edu. sa/whatamleh/en whatamleh@ksu. edu. sa Office hours by appointment or after class Slide 2

What is Statistics? • Statistics is the term for a collection of mathematical methods of organizing, summarizing, analyzing, and interpreting information gathered in a study Slide 3

Statistics • • Data Collection Summarizing Data Interpreting Data Drawing Conclusions from Data Slide 4

Data Collection • Designing experiments – Does aspirin help reduce the risk of heart attacks? • Observational studies – Patient attitude toward saudi nurses Slide 5

Summarizing and Interpreting Data • Grade distribution for a college course ( Growth and development course NUR 353) Slide 6

Drawing Conclusions • Quality control and improvement • Analysis of designed experiments • Analysis of observational studies Slide 7

Definition v Data observations (such as measurements, genders, survey responses) that have been collected Dr. Wajed Hatamleh Slide 8

Definition v Population The complete collection of all elements (scores, people, measurements, and so on) to be studied; the collection is complete in the sense that it includes all subjects to be studied Dr. Wajed Hatamleh Slide 9

Population Examples • Unemployment - Status of ALL employable people (employed, unemployed) in the KSA • Entry college Scores - scores of EVERY person that took the Entry college in KSA during 2009 • Responses of ALL currently enrolled underage college students as to whether they have consumed Arabic Coffee in the last 24 hours Slide 10

Sample A subset of the population data that are actually collected in the course of a study. Slide 11

Sample Examples • Unemployment - Status of the 1000 employable people interviewed. • College entry Scores - scores of 20 people that took the exam during 2009 • Responses of 538 currently enrolled underage college students as to whether they have consumed Arabic coffee in the last 24 hours Slide 12

Population vs. Sample Population Sample Slide 13

WHO CARES? In most studies, it is difficult to obtain information from the entire population. We rely on samples to make estimates or inferences related to the population. Slide 14

Definition v Parameter a numerical measurement describing some characteristic of a population parameter Dr. Wajed Hatamleh Slide 15

Definition v Statistic a numerical measurement describing some characteristic of a sample statistic Dr. Wajed Hatamleh Slide 16

Key Terms • 1. Population (Universe) – All Items of Interest • 2. Sample – Portion of Population • P in Population & Parameter • S in Sample & Statistic • 3. Parameter – Summary Measure about Population • 4. Statistic – Summary Measure about Sample Slide 17

Definition Variable: • Is a characteristics of an individual or object, it can be qualitative or quantitative. • Examples: (IQ level, Heart rate, age, gender, height, weight, blood pressure, income, eye color, cholesterol level) Dr. Wajed Hatamleh Slide 18

Qualitative and Quantitative Data( variable) Data can be further classified as being qualitative or quantitative. The statistical analysis that is appropriate depends on whether the data for the variable are qualitative or quantitative. In general, there are more alternatives for statistical analysis when the data are quantitative. Slide 19

Qualitative Variables ( Data) Labels or names used to identify an attribute of each element Often referred to as categorical data Can be either numeric or nonnumeric Appropriate statistical analyses are rather limited Examples: Gender, eye color, Slide 20

Quantitative variables (DATA) Quantitative data indicate how many or how much: discrete, if measuring how many continuous, if measuring how much Quantitative data are always numeric. Examples: Height of nursing student, patient weight and age, Slide 21

Working with Quantitative Data Quantitative data can further be described by distinguishing between discrete and continuous types. Dr. Wajed Hatamleh Slide 22

Definition Discrete data v result when the number of possible values is either a finite number or a ‘countable’ number (i. e. the number of possible values is 0, 1, 2, 3, . . . ) – – Example Number of siblings: 0, 1, 2, etc. (1. 2 is not possible) Number of hospital beds (129. 03 4 beds is not possible Dr. Wajed Hatamleh Slide 23

Definition v Continuous (numerical) data result from infinitely many possible values that correspond to some continuous scale that covers a range of values without gaps, interruptions, or jumps Example: The amount of milk that a cow produces; e. g. 2. 343115 gallons per day, weight, height. Dr. Wajed Hatamleh Slide 24

Definitions v Random Sample members of the population are selected in such a way that each individual member has an equal chance of being selected Dr. Wajed Hatamleh Slide 25

v Sample data must be collected in an appropriate way, such as through a process of random selection. v If sample data are not collected in an appropriate way, the data may be so completely useless that no amount of statistical torturing can salvage them. Dr. Wajed Hatamleh Slide 26

Random Sampling selection so that each has an equal chance of being selected Dr. Wajed Hatamleh Slide 27

Systematic Sampling Select some starting point and then select every K th element in the population Dr. Wajed Hatamleh Slide 28

Convenience Sampling use results that are easy to get Dr. Wajed Hatamleh Slide 29

Post test time • Are you ready? Dr. Wajed Hatamleh Slide 30

The population is A. A collection of observations. B. A collection of methods for planning studies and experiments. C. The complete collection of all elements. D. A sub-collection of members drawn from a larger group. Dr. Wajed Hatamleh Slide 31

The population is A. A collection of observations. B. A collection of methods for planning studies and experiments. C. The complete collection of all elements. D. A sub-collection of members drawn from a larger group. Dr. Wajed Hatamleh Slide 32

Which is an example of quantitative data? A. Weights of high school students. B. Genders of actors and actresses. C. Colors of the rainbow. D. Consumer ratings of a particular automobile (below average, and above average. ) Dr. Wajed Hatamleh Slide 33

Which is an example of quantitative data? A. Weights of high school students. B. Genders of actors and actresses. C. Colors of the rainbow. D. Consumer ratings of a particular automobile (below average, and above average. ) Dr. Wajed Hatamleh Slide 34

Which is not an example of continuous data? A. Temperature on a thermometer. B. Number of students in an algebra class. C. Mean weight of 100 flour sacks. D. Amount of water pumped from a pond per day. Dr. Wajed Hatamleh Slide 35

Which is not an example of continuous data? A. Temperature on a thermometer. B. Number of students in an algebra class. C. Mean weight of 100 flour sacks. D. Amount of water pumped from a pond per day. Dr. Wajed Hatamleh Slide 36

End of Chapter 1 Slide 37