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ad-work [718]
3 years ago
11

A random sample of 38 wheel chair users were asked whether they preferred cushion type A or B, and 28 of them preferred type A w

hereas only 10 of them preferred type B. Use a hypothesis test to assess whether it is fair to conclude that cushion type A is at least twice as popular as cushion type B.
Mathematics
1 answer:
-BARSIC- [3]3 years ago
7 0

Answer:

The statement that cushion A is twice as popular as cushion B cannot be verified

Step-by-step explanation:

From the question we are told that:

Sample size n=38

Type a size A X_a=28

Type a size B X_b=10  

Generally the probability of choosing cushion A P(a) is mathematically given by

P(a)=\frac{28}{38}

P(a)=0.73

Generally the equation for A to be twice as popular as B is mathematically given by

P(b)+2P(b)=3P

Therefore Hypothesis

Null H_0: p \leq \frac{2P}{3P} \\Altenative H_A:p>\frac{2P}{3P}

Generally the equation normal approx of p value is mathematically given by

z=\frac{x-np_0-0.5}{\sqrt{np_0(1-p_0)} }

z=\frac{28-(38*2/3)_0-0.5}{\sqrt{38*2/3*1/3} }

z=0.75

Therefore from distribution table

Pvalue=1-\theta (0.75)

Pvalue=0.227

Therefore there is no sufficient evidence to disagree with  the Null hypothesis H_0

Therefore the statement that cushion A is twice as popular as cushion B cannot be verified

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The table shows the number of medical clinics and the number of bowling alleys in several cities.
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Step-by-step explanation:

a) to create a scatter plot for the data, let data for clinics be x and that one for Bowling alleys be y then plot x values with corresponding y values on a graph tool.

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14                   12

1                      3

6                     6

5                     7

10                    7

6                      8

11                      9

8                      15

6                       7

16                     12

The scatter plot is attached below

b) a cluster is a group of data that is close together. From the plot, the ordered pairs forming a cluster are;

(6,6), (6,7), (5,7), and (6,8)

c) An outlier is a point in a plot which lies very far from the other values. In this case, point its  point (8,15)

d)The linear regression equation is one written in the form of

Y=a+bX where ;

Y=the dependent variable

X=the explanatory variable

a=the intercept, which is the value of y when x=o

b=slope of the line

The formula to apply here is ;

a=(∑y)(∑x²)-(∑x)(∑xy) / n(∑x²)-(∑x)² where n is number of samples

b= n(∑xy)-(∑x)(∑y) / n(∑x²)-(∑x)²

Form a table like below

<u>x                     y               xy               x²                 y²</u>

14                   12              168            196              144

1                      3                 3               1                   9

6                     6                 36            36                 36

5                     7                 35             25                49

10                    7                 70             100              49

6                      8                 48             36              64

11                      9                99             121               81

8                      15               120             64            225

6                       7                42               36              49

16                     12                192            256            144

83                     86              813           871               850------------sum

∑x=83   ∑y=86  ∑xy=813  ∑x²=871  ∑y²=850 and n=10

Applying the formula

a=(∑y)(∑x²)-(∑x)(∑xy) / n(∑x²)-(∑x)²

a=[(86 * 871) - (83*813) ]/ 10 (871-83²)

a=[74906 - 67479] / 10(-6018)

a=[7427] / -60180

a= - 0.1234

b= n(∑xy)-(∑x)(∑y) / n(∑x²)-(∑x)²

b=10(813)-(83*86) / 10 (871)-(83²)

b=[8130-7138] / 8710 -6889

b= 992/1821

b=0.5448

The liner regression equation will be;

Y=a+bX

Y= -0.1234 + 0.5448 X

e) correlation coefficient r is given by;

r= n∑xy - (∑x)(∑y) / √n(∑x²)-(∑x)² × √n(∑y²)-(∑y)²

r=10*813 - (83*86) / √10 *871 -83² ×√10*850 - 86²

r=8130-7138 / √8710-6889 × √8500-7396

r= 992 / (√1821 × √1104)

r=992/ 42.67×33.23

r=992/1417.9

r=0.6996

f) r² is important because it shows you the proportion of the variance of one variable that is predictable from the other variable.

r=0.6996

r²=0.6996² =0.4894

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