Marketing researchers were interested in how our perception of a food container's volume is affected by the shape(as measured by surface area) of a container. The article "Vital Dimensions in Volume Perception: Can the Eye Fool the Stomach?" (Journal of Marketing Research [1999]:313-326) gave data on the dimensions of 27 representative food items (e.g. San Miguel beer, Planters cheese balls, Gerber baby food and so on). Summary statistics for two variables (surface area and volume) are provided: Descriptive Statistics: surface area, volume Variable Mean StDev Minimum Q1 Median Q3 Maximum surface area (cm2) 45.69 39.31 16.32 23.77 33.8 48.06 174.42 volume (ml) 463.5 474.5 90.0 200.0 310.0 570.0 2330.0 Correlations: surface area, volume Pearson correlation of surface area and volume = 0.892

a) Find the least squares regression line of volume on surface area. Show all your work and circle the final regression equation. In your calculations, carry as many decimal places as your calculator can handle in the computation. Round your final answer to two decimal places.

b) Explain in simple language what the slope of this line tells you about the relationship between the two variables in the context of this problem. Be specific.

c) Find and interpret the value of R2 in context. What does this suggest about our ability to assess volume from a container's surface area?

Question 4 In the new 20th century physics, O Newtonian physics could still explain the structure of atoms. O Bohr used Planck's quantum theory to propose that electrons could jump between allowed orbits. O electrons could be anywhere in relation to an atom's nucleus. O the use of statistics in theories was thought to be ultimately worthless.Step 1 - Creating a module of functions for computing statistics As a first step, create a module with name StatisticsCalculation. To create the module, you can open a new python script. Save the script with the given name StatisticsCalculation.py. The statistics calculation module contains following functions. Your functions should do the computations directly without relying on any Python functions such as min, max, sum etc. calculateMean(data): The parameter data is a list of floating point values. This function will calculate and return the mean. Remember, data is a single list, it does not contain any nested list. calculateStdDev(data): Given a list of data values, calculate and return the standard deviation. Again remember, data is a single list, it does not contain any nested list. findMin (data): Given a list of data, this function will return the min. The parameter data is a single list, it does not contain any nested list. You can not use any built in function to calculate the minimum value. In this function, you need to implement your own function to find the minimum element in data. findMax(data): Given a list of data, this function will return the max. The parameter data is a single list, it does not contain any nested list. You can not use any built in function to calculate the maximum value. In this function, you need to implement your own function to find the maximum element in data.Individuals who want to work, but have given up searching for a job because of limited prospects, are know as . This group is included among , consisting of individuals who would like to work but have not looked for work during the past four weeks. discouraged workers ; marginally attached workers marginally attached workers ; discouraged workers OOOO frictionally unemployed ; the structurally unemployed discouraged workers ; minimally attractive workers Individuals who are involuntarily working part-time are considered O frictionally unemployed O structurally unemployed marginally employed O underemployed Using the U.S. Bureau of Labor Statistics calculation methods, excluding the groups identified in the questions above the true level of unemployment. overstates OOO understates accurately reflects Pravinus 60 Give Up & View Solution () Check Answer ()Next + Exit