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MathematicalMarketing Slide 2.1 Descriptive Statistics Chapter 2: Descriptive Statistics We will be comparing the univariate and matrix formulae for common.

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Presentation on theme: "MathematicalMarketing Slide 2.1 Descriptive Statistics Chapter 2: Descriptive Statistics We will be comparing the univariate and matrix formulae for common."— Presentation transcript:

1 MathematicalMarketing Slide 2.1 Descriptive Statistics Chapter 2: Descriptive Statistics We will be comparing the univariate and matrix formulae for common statistical quantities.

2 MathematicalMarketing Slide 2.2 Descriptive Statistics The Sample Mean Vector ScalarMatrix

3 MathematicalMarketing Slide 2.3 Descriptive Statistics Deviation Scores ScalarMatrix

4 MathematicalMarketing Slide 2.4 Descriptive Statistics Sum of Squares – Conceptual Formula A = DD ScalarMatrix

5 MathematicalMarketing Slide 2.5 Descriptive Statistics Sum of Squares – Hand Calculator Version ScalarMatrix

6 MathematicalMarketing Slide 2.6 Descriptive Statistics The Variance-Covariance Matrix ScalarMatrix

7 MathematicalMarketing Slide 2.7 Descriptive Statistics The Variance-Covariance Matrix  It’s a key matrix It summarizes the relationship between each pair of variables.  Its orderis m · m (where m is the number of vars)  It has lots of names variance matrix, covariance matrix, variance-covariance matrix

8 MathematicalMarketing Slide 2.8 Descriptive Statistics The Variance-Covariance Matrix Diagonal entries could be called The S matrix is symmetric There are m (m-1) / 2 unique off diagonal elements. There are m (m+1) / 2 unique elements.

9 MathematicalMarketing Slide 2.9 Descriptive Statistics The Diag(·) Function

10 MathematicalMarketing Slide 2.10 Descriptive Statistics The Square Root of a Diagonal Matrix A unique square root of a diagonal matrix may exist. For other square or rectangular matrices, the square root is not unique.

11 MathematicalMarketing Slide 2.11 Descriptive Statistics Z Scores ScalarMatrix

12 MathematicalMarketing Slide 2.12 Descriptive Statistics The Correlation Matrix


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