use https://www.eia.gov/dnav/pet/hist/LeafHandler.ashx?n=PET&s=MCRFPUS2&f=M to prepare data for time-series analysis Data needs to be melted All missing data must be removed Year should be converted to ‘string’ data type such that the Year is 4-digit string ‘Month’ and ‘Year’ should be combined and converted to Pandas datetime data type and promoted as index Unnecessary columns should be removed Prepare a time-series of quarterly average daily production data Prepare a time-series of annual average daily production data

Composite datatypes can only have multi row &multi column values in them Answers: Yes No

The condition of improper data yieldingmisleading results is referred to as  garbage in, garbage out. break-even point. uncontrollable variable. postoptimality.

A set of 11 data points is given in the attached Excel spreadsheet: PS5_prob1_FD_data.xlsx.Task: Perform numerical differentiation to approximate the second derivative of theprovided data. In doing so, address the following prompts:1. Write your own MATLAB code to perform an appropriate Finite Difference (FD)approximation for the second derivative at each point in the provided data. Note:You are welcome to use the “lowest order” approximation of the second derivative f ′′(x).a) “Read in” the data from the Excel spreadsheet using a built-in MATLAB com-mand, such as xlsread, readmatrix, or readtable – see docs for more info.b) Write your own MATLAB function to generally perform an FD approximationof the second derivative for an (arbitrary) set of n data points. In doing so, usea central difference formulation whenever possible.c) Call your own FD function and apply it to the given data. Report out/displaythe results.2. Reflect on your results by comparing to the exact solution.a) The…