Independent variable (x) • Also called predictor or explanatory or manipulated variable • the variable in regression that can be controlled or manipulated Dependent variable (y) • Also called the response variable • the variable that cannot be controlled or manipulated 7 [email protected] uitm. edu. my [email protected] uitm. edu. my 8 Dependent(x) Vs. Independent(y) • • • • Intentionally manipulated Controlled Vary at known rate Cause • • • • Intentionally left alone Measured Vary at unknown rate Effect Example: What affects a student’s arrival to class? Variables: • Type of School ? FSPPP, Business School, FSKM Type of Student ? Gender? CGPA? • Class Time ? Morning, Afternoon, Evening • Mode of Transportation ? Motorcycle, Car, UiTM bus 2 QMT412 Pn. Sanizah’s Notes 02/05/2013 9 [email protected] uitm. edu. my [email protected] uitm. edu. my 10 Scatter Plot (scatter diagram) • A scatter plot is used to show the relationship between two variables. • The scatter plot is a visual way to describe the nature of the relationship between the independent variable (x) and the dependent variable (y). • Interpreting scatter plots: ? ? ? ? Positive linear relationship Negative linear relationship Nonlinear relationship No relationship
Scatter Plot Examples Linear relationships y y Nonlinear (Curvilinear) relationships Positive x y x y x Negative x 11 [email protected] uitm. edu. my [email protected] uitm. edu. my 12 Scatter Plot Examples Strong relationships y y (continued) Weak relationships Scatter Plot Examples No relationship y (continued) x y y x y x x x x 3 QMT412 Pn. Sanizah’s Notes 02/05/2013 13 14 Example 1 (pg. 134) x 1 3 5 7 9 [email protected] uitm. edu. my Correlation Coefficient [email protected] uitm. edu. my • Draw a scatter diagram for the following data and state the type of relationship between the variables. 13 17
Correlation coefficient measures the strength and direction of a LINEAR relationship between a pair of random variables. y 0 5 11 14 19 22 30 The POPULATION correlation coefficient ? (rho) measures the strength of the association between the variables. The sample correlation coefficient r or ? s is an estimate of ? and is used to measure the strength of the linear relationship in the sample observations. 15 [email protected] uitm. edu. my [email protected] uitm. edu. my 16 Correlation Coefficient • “r” or “? s” indicates… ? strength of relationship (strong, weak, or none) ? direction of relationship ? ositive (direct) – variables move in same direction ? negative (inverse) – variables move in opposite directions • r ranges in value from –1. 0 to +1. 0. Moderate Weak Weak Moderate Do Variables Relate to One Another? Is teacher’s pay related to performance? Is exercise related to illness? Is CO2 related to global warming? Is TV viewing related to shoe size? Is shoe size related to height? Is height related to IQ? Is cigarettes smoked per day related to lung capacity? Positive Negative Positive Zero Negative -1. 0 -ve Perfect Positive -0. 8 -0. 5 0. 0 No Relationship +0. 5 +0. 8 +1. 0 +ve Perfect Very Strong Strong
Strong Very Strong 4 QMT412 Pn. Sanizah’s Notes 02/05/2013 17 [email protected] uitm. edu. my [email protected] uitm. edu. my 18 Positive correlation Two variables move in the same direction Negative correlation Two variables tend to go in the opposite direction 19 [email protected] uitm. edu. my Pearson Coefficient of Correlation • Both variables must be quantitative and normally distributed. • Calculation for r : r? Methods for Calculating Correlation Coefficient, r or ? s ?n ? ? ? xy ? ? x? y 2 2 ? x ? ?? x ? ? ? n? y 2 ? ?? y ? ? ? ? ? ? ? ? n 2 Pearson ProductMoment Correlation Coefficient Spearman Rank Correlation Coefficient or r? xy ? ?n ? ? x ? 2 ? ? y 2 ? ?? y ? 2 ? ?? x 2 ? ? ? ? ? ? n ? ?? n ? xy ? ? ? ? [email protected] uitm. edu. my 20 5 QMT412 Pn. Sanizah’s Notes 02/05/2013 Example 2 • Refer to Example 1. Compute Pearson coefficient of correlation and interpret the result. The Spearman rank correlation coefficient • Spearman’s rank correlation coefficient is a measure of association between two variables that are at least of ordinal scale (suitable for qualitative data). • Can also be applied to quantitative data but the variables must firsts be ranked and then only it is calculated based on these rankings. ? x ? ________ ? x 2 ? ________ ? ? ________ ? y 2 ? ________ ? xy ? ________ n ? ________ r? ? xy ? ?n ? ? x ? 2 ? ? ? y ? 2 ? ?? x 2 ? ? ? ?? y 2 ? ? ? ? n ? ? n ? xy ? ? ? ? ?s ? 1 ? 6? d 2 n ( n 2 ? 1) where: d = difference between two ranks n = number of pairs of observations [email protected] uitm. edu. my NOTE: Be careful with tied observations [email protected] uitm. edu. my 21 22 23 24 [email protected] uitm. edu. my How to calculate Spearman’s rank correlation coefficient? 1. 2. 3. 4. [email protected] uitm. edu. my Refer Example 5 pg. 140 Five students A, B, C, D, E are ranked in two subjects, statistics and computer programming with the following results.
Calculate the Spearman’s rank correlation coefficient. Subject Student Statistics Computer List each set of scores in a column. Rank the two sets of scores. Place the appropriate rank beside each score. Head a column d and determine the difference in rank for each pair of scores. (Note: Sum of the d column should always be 0) 5. Square each number in the d column and sum the values (? d 2). 6. Use the formula to calculate the correlation coefficient. d d2 ?s ? 1 ? 6? d 2 n ( n 2 ? 1) A B C D E 1 2 3 4 5 3 1 4 2 5 6 QMT412 Pn. Sanizah’s Notes 02/05/2013 25 26 Refer Example 6 pg. 141 x y [email protected] uitm. edu. my The Regression Line d2 [email protected] uitm. edu. my Rank of x, Rank of y, Rx Ry d=Rx-Ry 6. 0 6. 2 6. 5 6. 8 7. 0 7. 2 7. 5 7. 8 8. 0 8. 2 8. 4 8. 7 80 80 78 75 70 60 60 55 50 48 45 40 ? Regression indicates the degree to which the variation in one variable X, is related to or can be explained by the variation in another variable Y ? Once you know there is a significant linear correlation, you can write an equation describing the relationship between the x and y variables. ? This equation is called the line of regression or least squares line. • The equation of a line may be written as: y ? a ? bx • where b is the slope of the line and a is the y-intercept.
Analysis of Informative VS Persuasive
Analysis of Informative VS Persuasive.
Description The two main types of speeches we will be focusing on in this course are informative and persuasive speeches. Each speech type serves a separate purpose and goal. Search the web for examples of informative and persuasive speeches. You will find some excellent examples on this site: http://www.americanrhetoric.com. Locate and view one informative and one persuasive speech. In a 2–3-page paper, discuss the difference between informative and persuasive speeches. Can an informative speech be persuasive? Can a persuasive speech be informative? Why or why not? Assess the two speeches you viewed. Identify the two videos you viewed and briefly (in 2–3 sentences) describe the main focus of the speeches. Then discuss how well the speeches you viewed meet or fail to meet the criteria listed below. Provide examples to support your discussion. Each speech aligns with the purpose of its specific genre, the speaker’s end goal, and the final message the speaker wants to impart to the audience. Each speech manages to convince the audience of a certain viewpoint. Appropriate speech techniques are used that highlight each specific genre. Speech content is adequately tailored for audience considerations in terms of content, length, and delivery. Each speech is organized cohesively with a good introduction, body, and conclusion.
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