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__Shoe Size and Heights Class Activity __*(collect all StatCrunch outputs such as graphs and tables in a separate Word file named “Appendix 1” and make sure to include all group members’ names on the document and email to **flekr@newschool.edu**)*

*Names ___________________________ _____________________________*

*________________________________ _____________________________*

- Create a box plot of
*Shoe**Size*, including horizontal gridlines, and detecting any outliers. Answer the following questions about*Shoe**Size*from the box plot:

(a) Write down the Five Number Summary:

Min: Q1: Median: Q3: Max:

(b) What is the range? __ __

(c) What is the IQR?__ __

(d) Is the distribution symmetric, negatively skewed or positively skewed? Explain.

__ __

(e) Create a relative frequency histogram to confirm your answer to (d).

__ __

- Create two parallel box plots of
*Shoe**Size*comparing results based on (*grouped*)*by**Gender*. Are there any outliers within each group, if so, what are they? Make sure to highlight the corresponding cases. Describe as many differences between the distributions as you can based on the box plots. Are there any similarities?

__ __

- Confirm your answers by:

(a) Calculating the related summary statistics for each group (** n, Mean, SD, Median, IQR, Range, Min, Max, Q1, Q3, and Skewness**). Round your answers to two decimal places, where applicable.

*Summary Statistics for Shoe Size*

*Grouped by Gender*Gender |
n |
Mean |
Std. dev. |
Median |
IQR |
Range |
Min |
Max |
Q1 |
Q3 |
Skewness |

(b) Repeat the above calculation excluding the outlier(s) (by using the **Where** option, and excluding the outlier(s) by typing: **!Row_Selected** ). Describe all differences.

*Summary Statistics for Shoe Size w**ith Outliers Removed Grouped by Gender*

Gender |
n |
Mean |
Std. dev. |
Median |
IQR |
Range |
Min |
Max |
Q1 |
Q3 |
Skewness |

- Using the
**Frequency Table**function in StatCrunch, create two frequency tables for Shoe Size, one for Females and one for Males (excluding the outlier(s) and any missing entries denoted by “…”; in the**Where**option type**!Row_Selected and “Shoe Size (US Scale)”!=…**). Copy and paste the output in your Appendix file.

(a) Then calculate the quantities below:

# of Females with Show Sizes within 1 SD of the Mean: ___7.13-1.18 = 5.95__ —

__7.13+1.18=__=

**8.31****119**

(Mean – 1SD) (Mean + 1SD)

% of Females with Show Sizes within 1 SD of the Mean:

_____= = 0.73 =

**5.95**—**8.31****73%**

# of Females with Show Sizes within 2 SD of the Mean: _

__________— ________=

(Mean – 2SD) (Mean + 2SD)

% of Females with Show Sizes within 2 SD of the Mean: _

__________— ________ = = ___ = %

# of Females with Shoe Sizes within 3 SD of the Mean: _

__________— ________ =

% of Females with Shoe Sizes within 3 SD of the Mean: _

__________— ________ = = ___ = %

# of Males with Shoe Sizes within 1 SD of the Mean: _

__________— ________ =

% of Males with Shoe Sizes within 1 SD of the Mean: _

__________— ________ = = ___ = %

# of Males with Shoe Sizes within 2 SD of the Mean: _

__________— ________ =

% of Males with Shoe Sizes within 2 SD of the Mean: _

__________— ________ = = ___ = %

# of Males with Shoe Sizes within 3 SD of the Mean: _

__________— ________ =

% of Males with Shoe Sizes within 3 SD of the Mean: _

__________— ________ = = ___ = %

(b) According to the Empirical Rule, assuming that our data are normally distributed, what percentages should we have been expecting? How close were the observed percentages?

(c) Create two relative frequency histograms, Male and Female, overlaid with the Normal Curve, and describe the fit.

5). (a) Create a scatterplot of

*Heights*vs.

*Shoe*

*Size*(include all available data) displaying the regression line, and describe the

**. Are there any “unusual observations”?**

*direction and strength of the association*

(b) Find the correlation coefficient

*r*. What does it tell you about the relationship between the two variables? Hover over the line to see its equation; round all numbers to two decimal places:

**Correlation between Height (inches) and Shoe Size is:**

*r*=

**Regression Equation:**

**(c) What happens if you remove any “unusual points”? How does it affect the correlation coefficient? Is the regression equation affected?**

**Updated Correlation between Height (inches) and Shoe Size is:**

*r*=

**Updated Regression Equation:**

(d) Find the mean and SD for each of the two variables (not including the value(s) you removed in the previous step), and verify that (i) the

**is equal to**

*slope of the updated regression line**r*(SD*and (ii) the point (

_{y}/SD_{x})*Mean*,

_{x}*Mean*) is (approximately) on the regression line.

_{y}**Summary statistics:**

Column |
Mean |
Std. dev. |

Shoe Size | ||

Height (inches) |

** (i) **

*(ii)*