Price-demand. A company manufactures memory chips for microcomputers. Its marketing research department, using statistical techniques, collected the data shown in Table 8 , where p is the wholesale price per chip at which x million chips can be sold. Using special analytical techniques ( regression analysis ), an analyst produced the following price demand function to model the data: p x = 75 − 3 x 1 ≤ x ≤ 20 (a) Plot the data points in Table 8 , and sketch a graph of the price-demand function in the same coordinate system . (b) What would be the estimated price per chip for a demand of 7 million chips? For a demand of 11 million chips?
Price-demand. A company manufactures memory chips for microcomputers. Its marketing research department, using statistical techniques, collected the data shown in Table 8 , where p is the wholesale price per chip at which x million chips can be sold. Using special analytical techniques ( regression analysis ), an analyst produced the following price demand function to model the data: p x = 75 − 3 x 1 ≤ x ≤ 20 (a) Plot the data points in Table 8 , and sketch a graph of the price-demand function in the same coordinate system . (b) What would be the estimated price per chip for a demand of 7 million chips? For a demand of 11 million chips?
Price-demand. A company manufactures memory chips for microcomputers. Its marketing research department, using statistical techniques, collected the data shown in Table
8
, where
p
is the wholesale price per chip at which
x
million chips can be sold. Using special analytical techniques (regression analysis), an analyst produced the following price demand function to model the data:
p
x
=
75
−
3
x
1
≤
x
≤
20
(a) Plot the data points in Table
8
, and sketch a graph of the price-demand function in the same coordinate system.
(b) What would be the estimated price per chip for a demand of
7
million chips? For a demand of
11
million chips?
Definition Definition Statistical method that estimates the relationship between a dependent variable and one or more independent variables. In regression analysis, dependent variables are called outcome variables and independent variables are called predictors.
Consider a sample with data values of 27, 25, 20, 15, 30, 34, 28, and 25. Compute the range, interquartile range, variance, and standard deviation (to a maximum of 2 decimals, if decimals are necessary).
Range
Interquartile range
Variance
Standard deviation
Could you explain this using the formula I attached and polar coorindates
1: Stanley Smothers receives tips from customers as a standard component of his weekly pay. He was paid $5.10/hour by his employer and received $305 in tips during the
most recent 41-hour workweek.
Gross Pay = $
2: Arnold Weiner receives tips from customers as a standard component of his weekly pay. He was paid $4.40/hour by his employer and received $188 in tips during the
most recent 47-hour workweek.
Gross Pay = $
3: Katherine Shaw receives tips from customers as a standard component of her weekly pay. She was paid $2.20/hour by her employer and received $553 in tips during the
most recent 56-hour workweek.
Gross Pay = $
4: Tracey Houseman receives tips from customers as a standard component of her weekly pay. She was paid $3.90/hour by her employer and received $472 in tips during
the most recent 45-hour workweek.
Gross Pay = $
Chapter 2 Solutions
Finite Mathematics for Business, Economics, Life Sciences and Social Sciences
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Time Series Analysis Theory & Uni-variate Forecasting Techniques; Author: Analytics University;https://www.youtube.com/watch?v=_X5q9FYLGxM;License: Standard YouTube License, CC-BY