Step 1 :(i) To find the sample mean $\bar{x}$, we add up all the values and divide by the number of values. For the standard deviation s, we use the formula for the sample standard deviation, which is the square root of the variance. The variance is the average of the squared differences from the mean. The sample mean $\bar{x}$ is calculated as follows: $\bar{x} = (4.9 + 4.2 + 4.5 + 4.1 + 4.4 + 4.3) / 6 = 4.4$ The standard deviation s is calculated as follows: $s = \sqrt{[(4.9-4.4)^2 + (4.2-4.4)^2 + (4.5-4.4)^2 + (4.1-4.4)^2 + (4.4-4.4)^2 + (4.3-4.4)^2] / (6-1)}$ $s = \sqrt{0.25 + 0.04 + 0.01 + 0.09 + 0 + 0.01 / 5}$ $s = \sqrt{0.08} = 0.28$ (rounded to two decimal places) (ii) To determine whether the population mean RBC count for this patient is lower than 4.66, we can perform a one-sample t-test. The null hypothesis is that the population mean is 4.66, and the alternative hypothesis is that the population mean is less than 4.66. The level of significance is $\alpha = 0.05$. This means that we are willing to accept a 5% chance of rejecting the null hypothesis when it is true. To perform the t-test, we calculate the t-value as follows: $t = (\bar{x} - \mu) / (s / \sqrt{n})$ $t = (4.4 - 4.66) / (0.28 / \sqrt{6})$ $t = -2.79$ (rounded to two decimal places) We then compare the t-value to the critical t-value for a one-tailed test with 5 degrees of freedom (n-1) and a significance level of 0.05. The critical t-value is approximately -1.476. Since our calculated t-value is less than the critical t-value, we reject the null hypothesis and conclude that the data indicate that the population mean RBC count for this patient is lower than 4.66.