–Regression analysis is used to –Predict the value of a dependent variable (Y) based on the value of at least one independent variable (X) –Explain the impact of changes in an independent variable on the dependent variable – –Dependent variable (Y): the variable we wish to predict or explain (response variable) – –Independent variable (X): the variable used to explain the dependent variable (explanatory variable) – –Simple linear regression: –Only one independent variable, X –Relationship between X and Y is described by a linear function Changes in Y are assumed to be caused by changes in X
Why Self-Marketing? Week 5 Online Seminar Assignment Two Overview Self-Marketing Process Career Path Analysis, Goals and Objectives. Assignment 2 Overview Self-Marketing Plan (60% of overall Marks) Report Format Minimal References 2,000 Words (Approximately) Due Friday 29th January 12 Noon Extensions only granted on well-documented grounds Traditional Marketing Mix Expanded 7 Ps Marketing Mix Griffith Business School Linking Assignments 1 and 2: Self-Marketing Process Self-Marketing Plan • Draws on the self-analysis undertaken in Assignment 1 • Produce a marketing plan following the Self-Marketing Process in Chapter 12 • Please note this is NOT just copying and pasting from Assessment 1 • Must strategically evaluate your skills and attributes, your personality and beliefs to determine which characteristics are valuable in communicating an overarching story of your value • What is the best way to market yourself? Self-Marketing Plan Report Structure Title Page Executive Summary Table of Contents Table of Tables (if applicable) Table of Figures (if applicable) Introduction Career Path Analysis (Chapters 12 and 13) Career Goals (Chapters 12 and 13) Self-Positioning (Chapters 13 and 14) Self-Design (Chapters 15 to 17) Self-Promotion (Chapters 18 to 20) Evaluation and Contingencies (Chapter 21) Conclusion References Appendices (video pitch and online portfolio) Exercise your own judgment in distributing the number of words. Refer to the Marking Rubric and determine which sections carry more weight. So you know these sections need more elaboration Career Path Analysis Career Path Analysis Provide a comprehensive narrative describing your targeted career Record everything you know about your industry/career E.g., Qualifications needed, required skills, where the jobs are located, leading organisations within the industry, applicant competitiveness, dress code, ethics codes, breadth and depth of industry You MUST go beyond short term focus Career Path Analysis (Cont.) Career Path Analysis How will your career path look like in 5 to 10 years? What is the general career path in your industry? How long does it take to progress from one role to another? Are you able to progress with ease or are there difficulties involved? What are some of the additional skills and experience you would need as you progress through? How you might present your career path Career Goals and Objectives A Career Goal is presented as a broad statement of where you want to go (or what you want to be) in your career. Career Objectives represent the shorter-term goals or steps you need to achieve in order to be successful in achieving your Career Goal. Career Goals and Objectives Articulate your career goal and share with the class. Example: Career Goals and Objectives Writing SMART objectives Writing SMART objectives Writing SMART objectives Using the SMART framework, construct your objectives. Final Tip Your career goals and objectives must match your career path. Career Path is the Roadmap. Career Goal is the Destination. Career Objectives are the Signposts. For Next Week Have an awesome week !!! Write up and present your Career Path Analysis Write up and present your Goals and Objectives Watch Videos 8, 9 and 10 Read Chapters 12 and 13 Week 5 Online Seminar �Assignment 2 Overview Traditional Marketing Mix Expanded 7 Ps Marketing Mix Slide Number 5 Slide Number 6 Slide Number 7 �Self-Marketing Plan �Self-Marketing Plan �Career Path Analysis �Career Path Analysis (Cont.) �How you might present your career path �Career Goals and Objectives �Career Goals and Objectives Example:�Career Goals and Objectives Writing SMART objectives Writing SMART objectives Writing SMART objectives Final Tip For Next Week Slide 1 Chapter 12 Simple Linear Regression PowerPoint to accompany Berenson, Levine, Krehbiel, Watson, Jayne, Turner: Business Statistics © 2009 Pearson Education Australia Introduction to Regression Analysis Regression analysis is used to Predict the value of a dependent variable (Y) based on the value of at least one independent variable (X) Explain the impact of changes in an independent variable on the dependent variable Dependent variable (Y): the variable we wish to predict or explain (response variable) Independent variable (X): the variable used to explain the dependent variable (explanatory variable) Simple linear regression: Only one independent variable, X Relationship between X and Y is described by a linear function Changes in Y are assumed to be caused by changes in X Berenson, Levine, Krehbiel, Watson, Jayne, Turner: Business Statistics © 2009 Pearson Education Australia Types of Linear Relationships Y X Y X Y Y X X Strong relationships Weak relationships Berenson, Levine, Krehbiel, Watson, Jayne, Turner: Business Statistics © 2009 Pearson Education Australia Types of Non-linear Relationships Y X Y X No relationship Y Y X X Curvilinear relationships Berenson, Levine, Krehbiel, Watson, Jayne, Turner: Business Statistics © 2009 Pearson Education Australia Linear component Simple Linear Regression Model Population Y intercept Population slope coefficient Random error term Dependent variable Independent variable Random error component Berenson, Levine, Krehbiel, Watson, Jayne, Turner: Business Statistics © 2009 Pearson Education Australia Random error for this Xi value Y X Observed value of Y for Xi Predicted value of Y for Xi Xi Slope = β1 Intercept = β0 εi Simple Linear Regression Model Berenson, Levine, Krehbiel, Watson, Jayne, Turner: Business Statistics © 2009 Pearson Education Australia The simple linear regression equation provides an estimate of the population regression line Simple Linear Regression Equation (Prediction Line) Estimate of the regression intercept Estimate of the regression slope Estimated (or predicted) Y value for observation i Value of X for observation i The individual random error terms ei have a mean of zero Berenson, Levine, Krehbiel, Watson, Jayne, Turner: Business Statistics © 2009 Pearson Education Australia Least Squares Method b0 and b1 are obtained by finding the values of b0 and b1 that minimise the sum of the squared differences between actual values (Y) and predicted values ( ) b0 is the estimated average value of Y when the value of X is zero b1 is the estimated change in the average value of Y as a result of a one-unit change in X Berenson, Levine, Krehbiel, Watson, Jayne, Turner: Business Statistics © 2009 Pearson Education Australia Simple Linear Regression Example A manager of a local computer games store wishes to: examine the relationship between weekly sales and the number of customers making purchases over a 10 week period; and use the results of that examination to predict future weekly sales A random sample of 10 weeks sales records is selected Dependent variable (Y) = weekly sales in $1000s Independent variable (X) = number of customers Berenson, Levine, Krehbiel, Watson, Jayne, Turner: Business Statistics © 2009 Pearson Education Australia Sample Data for Model Weekly sales in $1000s (Y)Number of Customers (X) 2451400 3121600 2791700 3081875 1991100 2191550 4052350 3242450 3191425 2551700 Weekly sales model: scatter plot Berenson, Levine, Krehbiel, Watson, Jayne, Turner: Business Statistics © 2009 Pearson Education Australia Regression Using Excel Tools / Data Analysis / Regression Berenson, Levine, Krehbiel, Watson, Jayne, Turner: Business Statistics © 2009 Pearson Education Australia Excel Output Berenson, Levine, Krehbiel, Watson, Jayne, Turner: Business Statistics © 2009 Pearson Education Australia Graphical Presentation Weekly sales model: scatter plot and regression line Slope = 0.10977 Intercept = 98.248 Berenson, Levine, Krehbiel, Watson, Jayne, Turner: Business Statistics © 2009 Pearson Education Australia Interpretation of Coefficients b0, b1 b0 is the estimated average value of Y when the value of X is zero (if X = 0 is in the range of observed X values) Here, for no customers, b0 = 98.2483 which appears nonsensical. However, the intercept simply indicates that over the sample size selected, the portion of weekly sales not explained by number of customers is $98,248.33. Also note that X=0 is outside the range of observed values b1 measures the estimated change in the average value of Y as a result of a one-unit change in X Here, b1 = .10977 tells us that the average value of weekly sales increases by .10977($1000) = $109.77, on average, for each additional customer Berenson, Levine, Krehbiel, Watson, Jayne, Turner: Business Statistics © 2009 Pearson Education Australia Predict the weekly sales for the local store for 2000 customers: The predicted weekly sales for the local computer games store for 2000 customers is 317.85 ($1,000s) = $317,850 Predictions Using Regression Analysis Berenson, Levine, Krehbiel, Watson, Jayne, Turner: Business Statistics © 2009 Pearson Education Australia Interpolation vs. Extrapolation When using a regression model for prediction, only predict within the relevant range of data Relevant range for interpolation Do not try to extrapolate beyond the range of observed Xs Berenson, Levine, Krehbiel, Watson, Jayne, Turner: Business Statistics © 2009 Pearson Education Australia Measures of Variation Total variation is made up of two parts Total Sum of Squares Regression Sum of Squares Error Sum of Squares Measures the variation of the Yi values around their mean Y Explained variation attributable to the relationship between X and Y Variation attributable to factors other than the relationship between X and Y Berenson, Levine, Krehbiel, Watson, Jayne, Turner: Business Statistics © 2009 Pearson Education Australia Xi Y X Yi SST = (Yi - Y)2 SSE = (Yi - Yi )2 SSR = (Yi - Y)2 _ _ Y Y Y _ Y _ Measures of Variation Berenson, Levine, Krehbiel, Watson, Jayne, Turner: Business Statistics © 2009 Pearson Education Australia The coefficient of determination is the portion of the total variation in the dependent variable that is explained by variation in the independent variable The coefficient of determination is also called r-squared and is denoted as r2 Coefficient of Determination, r2 note: Berenson, Levine, Krehbiel, Watson, Jayne, Turner: Business Statistics © 2009 Pearson Education Australia r2 = 1 Examples of Approximate r2 values Y X Y X r2 = 1 Perfect linear relationship between X and Y 100% of the variation in Y is explained by variation in X r2 = 0 No linear relationship between X and Y The value of Y does not depend on X (None of the variation in Y is explained by variation in X) Y X Berenson, Levine, Krehbiel, Watson, Jayne, Turner: Business Statistics © 2009 Pearson Education Australia Y X Y X 0 < r2="">< 1 weaker linear relationships between x and y: some but not all of the variation in y is explained by variation in x examples 1="" weaker="" linear="" relationships="" between="" x="" and="" y:="" some="" but="" not="" all="" of="" the="" variation="" in="" y="" is="" explained="" by="" variation="" in="" x="">