Question

Gibbs Baby Food Company wishes to compare the weight gain of infants using its brand versus its competitor’s. A sample of 40 babies using the Gibbs products revealed a mean weight gain of 7.6 pounds in the first three months after birth. For the Gibbs brand, the population standard deviation of the sample is 2.3 pounds. A sample of 55 babies using the competitor’s brand revealed a mean increase in weight of 8.1 pounds. The population standard deviation is 2.9 pounds. At the .05 significance level, can we con- clude that babies using the Gibbs brand gained less weight? Compute the p-value and interpret it.

Answer 1

Answer:

$z=\frac{(7.6-8.1)-0}{\sqrt{\frac{2.3^2}{40}+\frac{2.9^2}{55}}}}=-0.936$  

The p value can be founded with this formula:

$p_v =P(z<-0.936)=0.175$  

Since the p value is higher than the significance level provided of 0.05 we have enough evidence to FAIL to reject the null hypothesis and we can't conclude that the true mean for the Gibbs brand is significantly lower than the true mean for the competitor

Step-by-step explanation:

Information given

$\bar X_{1}=7.6$ represent the mean for Gibbs products

$\bar X_{2}=8.1$ represent the mean for the competitor

$\sigma_{1}=2.3$ represent the population standard deviation for Gibbs

$\sigma_{2}=2.9$ represent the sample standard deviation for the competitor

$n_{1}=40$ sample size for the group Gibbs

$n_{2}=55$ sample size for the group competitor

$\alpha=0.05$ Significance level provided

z would represent the statistic

Hypothesis to verify

We want to check if babies using the Gibbs brand gained less weight, the system of hypothesis would be:  

Null hypothesis:$\mu_{1}-\mu_{2}=0$  

Alternative hypothesis:$\mu_{1} - \mu_{2}< 0$  

The statistic would be given by:

$z=\frac{(\bar X_{1}-\bar X_{2})-\Delta}{\sqrt{\frac{\sigma^2_{1}}{n_{1}}+\frac{\sigma^2_{2}}{n_{2}}}}$ (1)  

Replacing the info given we got:

$z=\frac{(7.6-8.1)-0}{\sqrt{\frac{2.3^2}{40}+\frac{2.9^2}{55}}}}=-0.936$  

The p value can be founded with this formula:

$p_v =P(z<-0.936)=0.175$  

Since the p value is higher than the significance level provided of 0.05 we have enough evidence to FAIL to reject the null hypothesis and we can't conclude that the true mean for the Gibbs brand is significantly lower than the true mean for the competitor