I need help completing the following table. Please summarize the results. (Attached is the case information )Thank you
Case Study Analysis: Questions for Summaryi Background The National Heart, Lung, and Blood Institute (NHIBI)1 created a teaching dataset that includes real but anonymized data collected as part of the Framingham Heart Study. The Framingharn Heart Study is one of the most inuential and longest running epidemiological studies of risk factors for cardiovascular disease ever run. The study started in 1948 and continues today to collect extensive data 'om original participants, their children, and their children's children. Much of what we know about cardiovascular disease was discovered by investigators involved in the Frarningharn Heart Study. In fact, studies to date using data collected in the Frarningham Heart study have resulted in over 3030 publications in high impact, peer-reviewed medical journals. The Fraruingharn Heart Study has been widely discussed in the media. WGBH in Boston produced a video documentary for PBS entitled \"The Hidden Epidemic: Heart Disease in America\" that details the history of heart disease inthis country and highlights the Frarningham Heart Study.1 In 2007, CBS News did a stay on the study, its participants, and its impact.3 Additionally, research results from the Framingham Heart Study are communicated widely, most recently highlighting the discovery of a gene that may promote obesity1 and new data showing declining rates of dementia.5 Interested readers can visit the Framingham Heart Study website for a detailed history of this incredible study and its marryr contributions to preventive medicine!5 Datasets for Analysis NHLBI created a longitudinal teaching dataset includes clinical, laboratory, and outcome data on n = 4434 participants. Each participant has between one and three observations-which represent examinations held approximately 6 years apart. There are a total of 11,627 observations in the full dataset. A detailed description of the Framingham Heart Study dataset and other public use datasets available from NHLBI are available on the NHLBI Biologic Specimen and Data Repository Information Coordinating Center (BioLINCO) website. Two datasets are available for analysis here-one is the complete dataset with n = 11,627 observations (or person-exams), and the second includes only data collected at the first examination for each participant (n = 4434). The two datasets are available as comma separated values (.csv) files for analysis in Excel, R, or other statistical computing packages. FHS-All.csv contains n = 11,627 observations and FHS-Exam1.csv contains n = 4434 observations. Variables The following variables are available in each dataset for analysis (extracted from the complete documentation file, available on the NHLBI BioLINCC website "). Variable Name Description Coding Details/Range RANDID Unique identification number for each participant 2248-9999312 SEX Participant sex 1 = Male, 2 = Female PERIOD Exam cycle 1, 2, 3 TIME Number of days since first (baseline) exam 0-4854 AGE Age at exam, years 32-81 SYSBP Systolic blood pressure, mmHg 83-295 DIABP Diastolic blood pressure, mmHg 30-150 BPMEDS Use of anti-hypertensive medication 0 = No, 1 = Yes CURSMOKE Currently smoking cigarettes 0 = No, 1 = Yes CIGPDAY Number of cigarettes smoked per day (non-smoker)-90 TOTCHOL Total serum cholesterol, mg/dL 107-696 HDLC* High density lipoprotein cholesterol, mg dl 10-189 LDLC* Low density lipoprotein cholesterol, mg/dL 20-565 BMI Body mass index = weight (kg)/height (m)- 14-57 7https://biolincc.nhibi.nih.gov/static/studies/teaching/framdoc.pdf?link_time=2016-07-06_14:21:55.514359 https://biolincc.nhibi.nih.gov/static/studies/teaching/framdoc.pdf?link_time=2016-07-06_14:21:55.514359GLUCOSE Serum glucose, mg dL 39-478 DIABETES Diabetes (glucose > 200 mg/dL or on treatment ) = No, 1 = Ye HEARTRTE Heart rate, beats/minute 37-220 PREVAP Prevalent angina pectoris 0 = No, 1 = Yes PREVCHD Prevalent coronary heart disease (CHD) 0 = No, 1 = Yes PREVMI Prevalent myocardial infarction (MI) 0 = No, 1 = Yes PREVSTRK Prevalent stroke 0 = No, 1 = Yes PREVHYP Prevalent hypertension 0 = No, 1 = Yes The following are outcome events coded I if the event occurred during the follow-up (only the first event is recorded). ANGINA Angina pectoris 0 = No, 1 = Yes HOSPMI Hospitalized for MI 0 = No, 1 = Yes MI FCHD Hospitalized for MI or fatal CHD 0 = No, 1 = Yes ANYCHD Any coronary heart disease event 0 = No, 1 = Yes STROKE Stroke 0 = No, 1 = Yes CVD Cardiovascular disease 0 = No, 1 = Yes HYPERTEN Hypertension 0 = No, 1 = Yes DEATH Death from any cause 0 = No, 1 = Yes The following are numbers of days from the first (baseline) exam to the first event during the follow-up. If no event occurred, time is end of follow-up, death, or last known contact date. TIMEAP Time from baseline to first angin TIMEMI Time from baseline to first myocardial infarction TIMEMIFC Time from baseline to first MI or fatal CHD TIMECHD Time from baseline to first CHD TIMESTRK Time from baseline to first stroke TIMECVD Time from baseline to first cardiovascular disease TIMEHYP Time from baseline to first hypertension TIMEDTH Time from baseline to death *Available only at period = 3 exam, missing otherwiseDesign, conduct and summarize results of the analyses outlined below using data collected in the Framingham Heart Study using FHS-Examl, the dataset that includes one observation per participant. Analytic approaches and coding for solutions are detailed in the Excel file 1. Describe the study sample. Complete the following table to describe the study sample using data collected at the first examination for each participant (n = 4434). Summarize your results in three to four sentences. Patient Characteristic* Total Sample (n = 4434) Age, years Male sex Systolic blood pressure, mmHg Hypertension Use of anti-hypertensive medication Current smoker Total serum cholesterol, mg/dL Serum Glucose Stroke * Mean (Standard deviation) or n (%)AutoSave O Off) case study analysis data (6) (1) . Search File Home Insert Page Layout Formulas Data Review View Help i) UPDATES AVAILABLE Updates for Office are ready to be installed, but first we need to close some apps. Update now -27 X V A B D E G H K M N O AGE SEX SYSBP HYPERTEN BPMEDS CURSMOKE TOTCHOL GLUCOSE STROKE Analysis Steps/Notes 1 Rearrange columns of data to keep only the variables for analysis (not required) 2 Compute means and standard deviations for continuous variables using AVERAGE(range) and STDEV(range) functions 3 Compute n(%) for dichotomous variables using COUNT(range) and COUNTIF(range, criteria) functionsAutoSave (C Off) case study analysis data(6) (1) - Search File Home Insert Page Layout Formulas Data Review View Help i UPDATES AVAILABLE Updates for Office are ready to be installed, but first we need to close some apps. Update now P24 v fx C D E G H K L M N O Q 1 SYSBP HYPERTEN BPMEDS CURSMOKE TOTCHOL GLUCOSE STROKE Analysis Steps/Notes Keep only the variables for analysis, and move SEX to column A (not required) 2 Sort data by SEX by highlighting data and clicking on Data-Sort and selecting SEX as the variable (column) to sort by (smallest to largest) 3 Compute means and standard deviations for continuous variables using AVERAGE(range) and STDEV(range) functions - note that ranges are modified to perform computations on selected subsets of men and women 4 Compute n(%) for dichotomous variables using COUNT(range) and COUNTIF(range, criteria) functions modifying ranges accordingly Data set Q1 Q2 Q3 Q4AutoSave C Off) H - : case study analysis data(6) (1) . Search File Home Insert Page Layout Formulas Data Review View Help i UPDATES AVAILABLE Updates for Office are ready to be installed, but first we need to close some apps. Update now 132 X v fx A B D G H 1 GLUCOSE AGE SEX SYSBP TOTCHOL CURSMOKE DIABETES Analysis Steps/Notes W N 1 Keep only the variables for analysis (not required) 2 Exclude participants with missing data on analysis variables n=397 are exclued for missing Serum Glucose, and an additional n=10 are excluded for missing TOTCHOL. Thus n=4027 3 Conduct simple linear regression analyses relating each predictor to Glucose using the Regression tool in the Data Analysis toolpak 4 Conduct multivariable linear regression analysis relating all predictors to Glucose using the Regression tool in the Data Analysis toolpak Note SEX is coded 1=male and 2=female. The regression coefficients reflect the change in Glucose associated with a one unit change in SEX (or the effect of female sex). To summarize the effect of male sex, we multiply the regression coefficient by -1 to reflect the effect of male sex. Data set Q1 Q2 Q3 Q4 +AutoSave C Of - ; case study analysis data (6) (1) - Search File Home Insert Page Layout Formulas Data Review View Help i UPDATES AVAILABLE Updates for Office are ready to be installed, but first we need to close some apps. Update now 133 : X V fx A B E G H 1 PREVCHD AGE SYSBP DIABP TOTCHOL BMI Analysis Steps/Notes W N Keep only the variables for analysis, and move PREVCHD to column A (not required) N Sort data by PREVCHD by highlighting data and clicking on Data-Sort and selecting PREVCHD as the variable (column) to sort by (smallest to largest) 3 Compute means and standard deviations for continuous variables using AVERAGE(range) and STDEV(range) functions - note that ranges are modified to perform computations on selected subsets with and without CHD Conduct t-tests to compare means between groups using the t-test function Data set Q1 Q2 Q3 Q4 +
