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amm1812
2 years ago
5

The resistance of a strain gauge is normally distributed with a mean of 100 ohms and a standard deviation of 0.3 ohms. To meet t

he specification, the resistance must be within the range 100±0.7 ohms. What proportion of gauges is acceptable? Round your answer to four decimal places.
Mathematics
1 answer:
zloy xaker [14]2 years ago
4 0

Answer: 0.6827

Step-by-step explanation:

Given : The resistance of a strain gauge is normally distributed .

Population mean : \mu=100

Standard deviation : \sigma=0.3

Let x be the random variable that represents the resistance of a strain gauge is normally distributed .

z-score : z=\dfrac{x-\mu}{\sigma}

To meet the specification, the resistance must be within the range 100±0.7 ohms i.e. (99.3,100.7).

For x = 100.7

z=\dfrac{100.7-100}{0.7}=1

For x = 99.3

z=\dfrac{99.3-100}{0.7}=-1

By using the standard normal distribution table, The proportion of gauges is acceptable is given by:-

P(99.3

Hence, the proportion of gauges is acceptable=0.6827

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The following estimated regression model was developed relating yearly income (y in $1000s) of 30 individuals with their age (x1
ira [324]

Answer:

R^2 = \frac{SSE}{SST}=\frac{384}{1200}=0.32

So the best option is  b. .42

Step-by-step explanation:

Previous concepts

Analysis of variance (ANOVA) "is used to analyze the differences among group means in a sample".  

The sum of squares "is the sum of the square of variation, where variation is defined as the spread between each individual value and the grand mean"  

The correlation coefficient is a "statistical measure that calculates the strength of the relationship between the relative movements of two variables". It's denoted by r and its always between -1 and 1.  And is defined as:

r=\frac{n(\sum xy)-(\sum x)(\sum y)}{\sqrt{[n\sum x^2 -(\sum x)^2][n\sum y^2 -(\sum y)^2]}}  

When we conduct a multiple regression we want to know about the relationship between several independent or predictor variables and a dependent or criterion variable.

Solution to the problem

If we assume that we have k independent variables and we have  j=1,\dots,j individuals, we can define the following formulas of variation:  

SS_{total}=\sum_{j=1}^n (y_j-\bar y)^2  

SS_{regression}=SS_{model}=\sum_{j=1}^n (\hat y_{j}-\bar y)^2  

SS_{error}=\sum_{j=1}^n (y_{j}-\hat y_j)^2  

And we have this property  

SST=SS_{regression}+SS_{error}  

We can find SS_{regression}= SST-SS_{error}=1200-384=816

The degrees of freedom for the model on this case is given by df_{model}=df_{regression}=k=2 where k =2 represent the number of variables.

The degrees of freedom for the error on this case is given by df_{error}=N-k-1=30-2-1=27.

The determination coefficient when we conduct a multiple regression is defined as:

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So the best option is  b. .42

4 0
2 years ago
9. A quality-control specialist for an electronics manufacturer estimates that 0.25% of the televisions produced each day by her
aleksandr82 [10.1K]

Answer:

33.75

Step-by-step explanation:

Given that the manufacturer produces an average of 450 televisions each day and 0.25% of the numbers produced each day are defective.

Therefore

0.25% of 450 will give the number of defective televisions produced per day

So

0.25% /100 x 450

0.0025 x 450

1.125

This means that 1.125 of televisions produced each day are defective.

Now to get the estimate of defective televisions in 30 days , multiply the number of defective televisions per day by 30 days seeing that the producer produces an average of 450 each day

So

1.125 x 30

33.75

33.75 televisions are defective in 30 days

6 0
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