# Stata Assignment Help

Certain data set was provided and statistical modelling methods were applied for analysis and result interpretation. The report has the detailed study as multiple linear regression statistical technique is used in stata assignment help. Initial evaluation is carried out using saturated models. Further tests like ANOVA F-test, Chi-square test were conducted to document and evaluate the presence of influential observations in stata homework help.** **

This study examines the nature of the relationship between fish intake and blood HDL‐cholesterol levels (if any), and evaluate the confounding influence of other variables.

The studyuses the dataset provided by a stata assignment help sample from the 1999-2002 National Health and Nutrition Examination Survey (NHANES). A race-stratified random sample was selected from a complete dataset for the variables included, and then modified to remove missing data. The random sampling based on race was setup to result in a race distribution similar to the US population. The data are restricted to no reported history of heart disease, and no reported use of prescription drugs for hyperlipidemia or inflammation. The dietary intakes are based on a 24‐hour diet recallinterview.

Bivariate analysis reveals significant associations between blood HDL and Fish Consumption, BMI(kg/m2), gender, smoking habits, race, physical activity, and education level (Table 1). Fish consumption showed a significant quadratic trend, while for the remaining variables linear trends were noticed. Age proved to have no significant association with HDL categories.

Considering the diet intakes, one found significant associations with Energy (kcal), Protein (gm), Carbohydrate (gm), Fat (gm), Cholesterol (gm), as well as for smoking habits (Table 2). In all cases liner trend are likely to be present. Iet content in Fiber (gm), Vitamin C (mg), and Selenium (mcg) showed no significant association with HDL.

A multivariate analysis was set up. To avoid multicollinearity, the continuous predictors were men centered.

An initial evaluation of collinearity was done using VIF. It turns out stata homework helpthat only one predictor has a value larger than the typical threshold of 10: the 24 hours intake of energy.

We used Allen-Cadymodified backward selection procedure for selecting the predictors that actually cause multicollinearity. Several variables (tfish, gender, age, bmi, pactive, and smoke) were pre-specified for forced inclusion, since they are of main interest for this study. Then, a threshold p-value of 0.20 was used for removal of variables of leastimportance (Table A1). Such variables are Blood ldl-collesterol, the “Non-Hispanic White” category for race, the Selenium intake, two educational categories (“High School” and “Some College/AA”), the intake of Cholesterol, and the one of Vitamines.

In the end, all remaining variables were under the .1 p-value threshold and were retained in the model.

With the new model, the linearity of the relation between the dependent variable and bmi, age, fish consumption, and blood triglyceride was tested. The results in Figure 1 indicate that in the case of BMI and tryglicerides the relation might be quadratic instead of linear (the loess curve seems to indicate such dependency).

The model is stata project help repeated and the two quadratic terms prove to be significant. The loess curve clearly indicates this time a quadratic relation for both predictors (Figure 2).

Figure 1. The first testing of linearity

Figure 2. Testing linearity of the relation with BMI and triglycerides: predicted valuesstata assignment help from model 2

The next step is to evaluate the normality andhelp with stata assignment homogeneity of variance assumptions for the dependent variable. The normality of help with stata homework residuals is showed in Figure 3. The only problem might be with the longer queue at the right. A Shapiro-Wilk test for normality also proves significant, indicating a potential normality problem.

Figure 3. Normality of residuals for model 2

Figure 4. Testing for homoscedasticity in model 2

Homoscedasticity is visually depicted in Figure 4. It looks like no constant variance can be found. Both White’s test and theBreusch-Pagan test prove significant. Therefore we conclude that heteroscedasticity is present, and a logarithmic transformation of the dependent variable is performed.

Influential points are assessed in the following. The dfbetas were saved and plotted in Figure 5. The red horizontal lines indicate the threshold of 2/sqrt(N). One may easily notice there are several points with values over the limit. A list of 10 of the influential point is provided atstata problems with answers the end of this report (Table A2).

Figure 5. DfBetas for model 3

Re-assessing the stata questions with answers relation between transformed HDL and each predictor, one may see significant associations for all considered independent variables, except for the daily intake of VitaminC and of Fiber (Tables 3).

Compared to the reference categories, the dependent variable increases when the subject is Female, Non-Hispanic Black, has low-moderate activity or high-moderate activity, and is College Graduate or above. Log(HDL) also increases with Alcohol consumption. Fat, Cholesterol, Selenium, LDL, protein, calories, being a smoker decrease HDL. High or low values in BMI are associated to high levels of HDL (quadratic dependency). The same applied to the relation between triglycerides and HDL.

Table 5 shows the predictors exclude by the Allen-Cady Modified Backwards Selection procedure. All other variables were retained in the model.

After controlling for other confounders, the impact of number of fish meals in last 30 days proves to be insignificant in determining HDL. All other predictor stata homework help preserve their impact, as already described. For instance, the quantity of fibres increases the levels of the dependent variable. One unit increase in the values of Fibres (that is 1 gm), leads to an increase of 10^{0.00208} mg/dL of blood HDL-cholesterol.

Table 6 summarizes the final model (model 4) which also evaluates the impact of the interaction between fish consumption and gender. Nothing changes with respect to the variables of interest. The fish menus remain insignificant and the interaction is insignificant at α=.05. If one would consider α=.10 as level of significance, the interaction become significant. Its interpretation says that, for women, on, average, each fish mealstata homework help brings an increase of 10^{0.00601} mg/dL in the value of HDL.

Table 1. Characteristics of the study sample by Blood HDLQuartiles | |||||||||

Characteristic |
HDL (mg/dL)Categories |
p-value |
|||||||

< 40 (n=439) | 41 to 48 (n=458) | 48 to 59 (n=433) | > 59 (n=436) | ||||||

Mean or% | SD | Mean or% | SD | Mean or% | SD | Mean or% | SD | ||

Fish Consumption (meals/30days) | 1.9 | 3.4 | 1.5 | 3.1 | 2.1 | 3.0 | 3.0 | 4.6 | < 0.001a 0.002c |

Age(years) | 34.2 | 8.2 | 34.8 | 8.1 | 34.2 | 8.4 | 34.4 | 8.3 | 0.6614a |

BMI(kg/m2) | 30.2 | 6.7 | 27.9 | 6.4 | 27.6 | 6.1 | 25.7 | 5.8 | < 0.001a < 0.001b |

Gender (%female) | 33.0 | 50.4 | 61 | 78.2 | < 0.001d | ||||

Smoker (%yes) | 38.7 | 29.7 | 25.4 | 22.5 | < 0.001d | ||||

Race/Ethnicity(%) | 0.005d | ||||||||

White | 63.8 | 58.3 | 61.2 | 65.1 | |||||

Black | 8.9 | 13.1 | 15.2 | 14.9 | |||||

Hispanic | 27.3 | 28.6 | 23.6 | 20.0 | |||||

Physical Activity(%) | 0.003d | ||||||||

Low | 26.2 | 21.5 | 19.7 | 22.0 | |||||

Low-Moderate | 42.4 | 49.0 | 54.8 | 49.4 | |||||

High-Moderate | 17.8 | 21.3 | 18.1 | 18.6 | |||||

High | 13.7 | 8.3 | 7.3 | 10.0 | |||||

Education Level(%) | < 0.001d | ||||||||

Less thanHS | 26.7 | 28.4 | 20.8 | 17.0 | |||||

HS/GED | 28.7 | 26.9 | 22.9 | 22.3 | |||||

Somecollege | 29.4 | 27.7 | 34.6 | 26.8 | |||||

College ormore | 15.3 | 17.0 | 21.7 | 33.9 | |||||

a. ANOVAF-test.
b. Test for linear trend afterANOVA. c. Test for quadratic stata homework help trend afterANOVA. d. Chi-squaretest. |

Table 2. 24-Hour diet intake profile of the study sample by Blood HDLQuartile | |||||||||

DietaryFactor |
HDL (mg/dL)Categories | p-value | |||||||

< 40 (n=439) |
41 to 48 (n=458) |
48 to 59 (n=433) |
> 59 (n=436) |
||||||

Mean or% | SE | Mean or% | SE | Mean or% | SE | Mean or% | SE | ||

Energy(kcal) | 2504,1 | 1037,9 | 2398,1 | 1060,4 | 2295,9 | 951,4 | 2230,2 | 875,9 | < 0.001a < 0.001b |

Protein(gm) | 92,1 | 47,1 | 84,5 | 52,7 | 83,4 | 39,9 | 80,4 | 37,7 | < 0.001a < 0.001b |

Carbohydrate(gm) | 320 | 138,5 | 309,4 | 139,2 | 284 | 129,3 | 272,9 | 124,1 | < 0.001a < 0.001b |

Fat(gm) | 332 | 300,4 | 288,6 | 241,5 | 296,5 | 222,8 | 284,1 | 204,4 | 0.004a < 0.001b |

Cholesterol(gm) | 84,6 | 48,1 | 79 | 42,4 | 77,1 | 40,8 | 74,6 | 35,6 | 0.016a 0.010b |

Fiber(gm) | 16,3 | 11,2 | 16,1 | 9,8 | 16,3 | 10,1 | 15,7 | 10,1 | 0.731a |

Vitamin C(mg) | 96,1 | 118,4 | 93,7 | 96 | 104,6 | 116,9 | 99,2 | 101,4 | 0.474a |

Selenium(mcg) | 120,2 | 65,8 | 112,7 | 75,7 | 110 | 62,2 | 109,3 | 63,1 | 0.064a |

Alcohol (%yes) | 20% | 40% | 30% | 50% | 30% | 50% | 40% | 50% | < 0.001d |

a. ANOVAF-test.
b. Test for linear trend afterANOVA. c. Test for quadratic trend afterANOVA. d. Chi-squaretest. stata assignment solution |

Table 3. Confounding Influence of Covariates on Fish Consumption Regression Coefficient | ||||

PotentialConfounder | b | % Change inba | p-valueb | |

None | 3.87000 | 39867% | < 0.001 | |

Age | -0.00017 | -102% | < 0.001 | |

BMI | -0.01466 | -241% | < 0.001 | |

Squared BMI | 0.00047 | -95% | < 0.001 | |

Gender (Female) | 0.18790 | 4557% | < 0.001 | |

Smoker (yes) | -0.07761 | -942% | < 0.001 | |

Race/Ethnicity | < 0.001 | |||

Non-Hispanic White | 0.02937 | 230% | 0.064 | |

Non-Hispanic Black | 0.07355 | 726% | 0.001 | |

PhysicalActivity | ||||

low-moderate activity | 0.05698 | 501% | 0.001 | |

high-moderate activity | 0.05196 | 448% | 0.013 | |

high activity | -0.02264 | -339% | 0.371 | |

EducationLevel | ||||

High School Grad/GED | 0.01623 | 95% | 0.391 | |

Some College or AA degree | 0.03381 | 307% | 0.064 | |

College stata assignment help Graduate or above | 0.12986 | 1464% | < 0.001 | |

DietaryEnergy | -0.00003 | -100% | < 0.001 | |

DietaryProtein | -0.00061 | -106% | < 0.001 | |

DietaryCarbohydrate | -0.00028 | -103% | < 0.001 | |

DietaryFat | -0.00053 | -105% | 0.001 | |

DietaryCholesterol | -0.00006 | -101% | 0.040 | |

DietaryFiber | -0.00076 | -108% | 0.241 | |

Dietary VitaminC | 0.00001 | -100% | 0.858 | |

DietarySelenium | -0.00034 | -103% | 0.001 | |

Blood Triglyceride | -0.00163 | -121% | < 0.001 | |

+squared | 0.00001 | -100% | < 0.001 | |

Blood LDL-cholesterol | -0.00054 | -106% | 0.005 | |

Alcohol | 0.04715 | 401% | 0.001 | |

a. (b(confounder) – b(fish))/b(fish) as%.
b. P-value for fish beta coefficient for in a model also containing the potential confounder. Reference categories are: Male, Non-smoker, Hispanic, low-physical activity, Less Than High School stata assignment solution |

Table 4. Allen-Cady ProcedureResults | ||||||

Predictor in RankOrdera |
Coefficient EstimateP-Valueb | |||||

Step1 | Step2 | Step3 | Step4 | Step5 | Step6 | |

(mean centered) tchol | 0.1664 | |||||

(mean centered) talco | 0.3064 | |||||

educ_3 | 0.4409 | |||||

educ_2 | 0.6747 | |||||

(mean centered) tsele | 0.8612 | |||||

race_2 | 0.9394 | |||||

a. Most important to leaststata assignment help important.
b. P-value for beta coefficient stata homework solution for predictors at each step in the backward selection with p=0.1 as the retentioncriteria. |

Table 5.Regression model for the association of blood HDL- cholesterol with fish consumption pay for stata homework adjusting for confounding by demographic characteristics and dietaryfactors. | ||||

Predictor | b | 95%CI | p-value | |

Number of fish meals in last 30 days |
0.00135 |
[-0.00183 |
0.00454] |
0.404 |

Gender (Female) | 0.20169 | [0.17645 | 0.22693] | < 0.001 |

Age | 0.00069 | [-0.00070 | 0.00207] | 0.330 |

BMI | -0.01075 | [-0.01303 | -0.00846] | < 0.001 |

squared BMI | 0.00026 | [0.00011 | 0.00042] | 0.001 |

low-moderatestata assignment help activity | 0.05014 | [0.02171 | 0.07857] | 0.001 |

high-moderate activity | 0.05050 | [0.01541 | 0.08559] | 0.005 |

high activity | 0.05873 | [0.01428 | 0.10318] | 0.010 |

Smoker (yes) | -0.05813 | [-0.08451 | -0.03175] | 0.000 |

Blood LDL-cholesterol | 0.00034 | [-0.00001 | 0.00068] | 0.055 |

Non-Hispanic Black | 0.05203 | [0.01769 | 0.08637] | 0.003 |

Blood Triglyceride – suared | 0.00000 | [0.00000 | 0.00001] | < 0.001 |

Blood Triglyceride | -0.00110 | [-0.00135 | -0.00086] | < 0.001 |

College Graduate or above | 0.08836 | [0.06024 | 0.11647] | < 0.001 |

Dietary Energy | 0.00020 | [0.00015 | 0.00025] | < 0.001 |

Dietary Protein | -0.00098 | [-0.00141 | -0.00055] | < 0.001 |

Dietary Carbohydrate | -0.00102 | [-0.00124 | -0.00080] | < 0.001 |

Dietary Vitamin C | 0.00010 | [-0.00001 | 0.00022] | 0.081 |

Dietary Fat | -0.00155 | [-0.00212 | -0.00098] | < 0.001 |

Dietarystata assignment help Fiber | 0.00208 | [0.00064 | 0.00352] | 0.005 |

Constant | 3.69964 | [3.66676 | 3.73251] | < 0.001 |

Table 6.Regression model for the association of blood HDL- cholesterol with fish consumption and the interaction with gender, with adjustment for stata homework helpconfounding by demographic characteristics and dietaryfactors. | ||||

Predictor | b | 95%CI | p-value | |

Number of fish meals in last 30 days |
-0.00275 |
[-0.00894 |
0.00344] |
0.384 |

Gender (Female) | 0.19124 | [0.16318 | 0.21930] | 0.000 |

fish meals # female |
0.00601 |
[-0.00115 |
0.01317] |
0.100 |

Age | 0.00079 | [-0.00060 | 0.00217] | 0.266 |

BMI | -0.01061 | [-0.01291 | -0.00831] | 0.000 |

squared BMI | 0.00027 | [0.00012 | 0.00043] | 0.001 |

low-moderate activity | 0.04914 | [0.02063 | 0.07765] | 0.001 |

high-moderate stata assignment help activity | 0.04994 | [0.01476 | 0.08512] | 0.005 |

high activity | 0.05405 | [0.00956 | 0.09854] | 0.017 |

Smoker (yes) | -0.05402 | [-0.08040 | -0.02763] | 0.000 |

Blood LDL-cholesterol | 0.00036 | [0.00001 | 0.00070] | 0.042 |

Non-Hispanic Black | -0.01709 | [-0.04142 | 0.00723] | 0.168 |

Blood Triglyceride – suared | 0.00000 | [0.00000 | 0.00001] | 0.000 |

Blood Triglyceride | -0.00116 | [-0.00141 | -0.00092] | 0.000 |

College stata project help Graduate or above | 0.09022 | [0.06123 | 0.11920] | 0.000 |

Dietary Energy | 0.00020 | [0.00015 | 0.00025] | 0.000 |

Dietary Protein | -0.00104 | [-0.00155 | -0.00054] | 0.000 |

Dietary Carbohydrate | -0.00101 | [-0.00123 | -0.00079] | 0.000 |

Dietary Vitamin C | 0.00012 | [0.00000 | 0.00023] | 0.048 |

Dietary Fat | -0.00152 | [-0.00209 | -0.00095] | 0.000 |

Dietary Fiber | 0.00181 | [0.00037 | 0.00324] | 0.014 |

Constant help with stata assignment | 3.72018 | [3.68427 | 3.75610] | 0.000 |

# Additional Tables

Table A1. Allen-Cady ProcedureResults for Model 1 | ||||

predictor | Coefficient EstimateP-ValuedStep1 | |||

(mean centered) tvc | 0.1082 | |||

(mean centered) tchol | 0.2047 | |||

educ_2 | 0.5402 | |||

educ_3 | 0.3424 | |||

(mean centered) tsele | 0.8966 | |||

race_2 | 0.9216 | |||

(mean centered) ldl stata assignment for money | 0.9494 | |||

d. P-value for beta coefficientpay for stata assignment for predictors at each step in the backward selection with p=0.1 as the retentioncriteria. |

Table A2. List of first 10 influential stata homework for money points in model 3