A harris interactive survey for intercontinental hotels resorts

A Harris Interactive Survey For InterContinental Hotels &Resorts Asked Respondents,

A Harris Interactive survey for InterContinental Hotels &Resorts asked respondents, “When traveling internationally, doyou generally venture on your own to experience culture, orstick with your tour group and itineraries?” The survey foundthat 23% of the respondents stick with their tour group (USAToday). Consider a random sample of 6 international travelers.

1. MINITAB probability table (must be included to receive credit for 4 & 5)
2. What is the probability that at least 2 will stick with their tour?
3. What is the probability that none will stick with their tour?
4. How many international travelers do you expect will stick with theirtour?

In this unit we explore how the concept of linear regression is used to model data from a sample. We use MS Excel 2010 to generate a line of best fit, a linear equation that approximates the data values, and then we use the equation to predict other data values.

To complete this discussion, find a YouTube video that gives an example of how a linear equation line of best fit is determined from a data set using Microsoft Excel. Post the video and answer the following questions:

1) What does the data represent?

2) What is the independent variable (x-values)?

3) What is the dependent variable (y-values)?

4) What is the equation for the line of best fit that is determined using the Microsoft Excel Regression Analysis tool on the data?

5) Interpret the coefficients in this equation (the number in front of x, b1, and the constant, bo).

Notes on interpreting the coefficients:

· The intercept, b0 , is the value of y if the value of x were 0.

· The coefficient of the independent variable, b1 , is the amount by which y changes if the value of x is increased by 1.

6) What is the value of R2 ("R-squared" in Excel)? Interpret this.

Notes on interpreting R2:

· If R2 > 0.90, then the variable x is a VERY GOOD predictor of the variable y . Over 90% of the variance in the value of y can be explained by changes in the value of x. This equation should be used to predict y-values from x-values.

· If R2 is more than 0.70 but less than 0.90, then the variable x is an acceptable predictor of the variable y . Between 70% and 90% of the variance in the dependent variable y can be explained by changes in the value of the independent variable x. This equation can be used to predict y-values from x-values, with the understanding that some error will exist in predictions .

· If R2 < 0.70, then the variable x is a POOR predictor of the variable y. This equation should not be used to predict y-values from x-values.