I need help with anaylizing the data. Under the instructions, I put a link in to the Excel data that needs to be used to answer the questions. It is located on top of page 4. Thank you in advance.

Hemoglobin: Ideal Hemoglobin A1C levels for patients are 6 or 7, a value of 8 or 9 merits concern, values 10 and up are considered severely uncontrolled, while values less than 6 are rare in diabetic patients. 4 and 5 can be found normally in patients that are not diabetic.

Blood Sugar: Glucose levels under 70 are considered low, between 70 and 110 is considered normal, 111 to 170 is considered moderately high, and values above 170 are considered high. There is some debate on the cut points, however, these are the values used to categorize glucose levels in this study.

Glucose _Range: This is a categorical variable describing the group into which the patient's glucose level places them: low, normal, moderately high, and high.

Glucose_Group: This is a numeric variable containing the same information as the Glucose_Range, however, the numeric value assigned to each group can be used in analysis that requires a ratio or interval level of measurement. Low is assigned a 1, Normal is assigned a 2, Moderately High is assigned a 3, and High is assigned a 4.

Carbohydrates: Diabetic patients try to consume 14 servings of carbohydrates daily where each serving is approximately 15 grams. This study tracks the average grams of carbohydrates consumed on a daily basis by these patients

Review the Microsoft Excel information on ANOVA:https://support.microsoft.com/en-us/office/use-the-analysis-toolpak-to-perform-complex-data-analysis-6c67ccf0-f4a9-487c-8dec-bdb5a2cefab6

Scroll down to ANOVA, and click to expand:

\fAnova The Anova analysis tools provide different types of variance analysis. The tocl that you should use depends on the number of factors and the number of samples that you have from the populations that you want to test Anova: Single Factor This tool performs a simple analysis of variance on data for twe or more samples. The analysis provides a test of the hypothesis that each sample is drawn from the same underlying probability distribution against the alternative hypothesis that underlying probability distributions are not the same for all samples. If there are anly two samples, you can use the worksheet function T.TEST. With more than two samples, there is no convenient generalization of T.TEST, and the Single Factor Anova model can be called upcn instead. Anova: Two-Factor with Replication This analysis tool is useful when data can be classified along two different dimensions. For example, in an experiment tc measure the height of plants, the plants may be given different brands of fertilizer (for example, A, B, C) and might also be kept at different temperatures (for example, low, high). For each of the six possible pairs of {fertilizer, temperature}, we have an equal number of observations of plant height. Using this Anova tool, we can test: n Whether the heights of plants for the different fertilizer brands are drawn from the same underlying population. Temperatures are ignored for this analysis. m Whether the heights of plants for the different temperature levels are drawn from the same underlying population. Fertilizer brands are ignored for this analysis