Assuming that during the growing season it is predicted that there will be 67 days of sun, what will the corn yield be in bushels per acre?
Assuming that we have a data set that includes sales data for every customer over the course of several years and we wanted to use this data to predict future sales which would be the most appropriate technique to investigate?
c) An image analyst obtains some new images and wants to automatically detect the number of distinct objects in the image. b) Angles as measured in degrees between 0 and 360. c) Bronze, Silver, and Gold medals as awarded at the Olympics.
He does not have any prior information about these objects. Q.2) Classify the following attributes as binary, discrete, or continuous.
d) Predicting the outcomes of tossing a (fair) pair of dice. Also, classify them as qualitative (nominal or ordinal) or quantitative (interval or ratio).
e) Predicting the future stock price of a company using historical records. Some cases may have more than one interpretation, so briefly indicate your reasoning if you think there may be some ambiguity. Answer: Discrete, quantitative, ratio a) b) c) d) e) Time in terms of AM or PM. Bronze, Silver, and Gold medals as awarded at the Olympics. Management Assignment Help Business Assignment Help MS Office Assignment Help English Assignment Help Data Flow Diagram Assignment Help Psychology Assignment Help Physiology Homework Help Math Homework Help Power Point Presentation Assignment Help Marketing Plan Help Auto Cad Assignment Help Do My Homework Computer Science Assignment Help Excel Assignment Help SPSS Assignment Help Nursing Assignment Help Do My Assignment Term Paper Help History Assignment Help Health Science Homework Help Marketing Assignment Help Thesis & Dissertation Help Content Writing Help Conflict Management Assignment Help Law Assignment Help Software Engineering Assignment Help Econometric Assignment Help Programming Assignment Help Research Paper Help SQL Assignment Help Homework Help Statistics Assignment Help Business Plan Help International Business Assignment Help MATLAB Assignment Help Disclaimer: The reference papers or solutions provided by serve as model papers or solutions for students or professionals and are not to be submitted as it is.These papers are intended to be used for research and reference purposes only.03/03/2019 is the due date for submitting the remaining implementation of T1 and the remaining part in T2.Cluster Analysis (Download from here ) Dataset: Right click here and select "save link as" to download Note: If the "Brighkite_total Checkins.txt" file is not opening in your computer because of its heavy size, there is no need to do that.The objective of this course is to set up the foundational concepts and techniques in databases and data mining with particular emphasis on analyzing different kinds of data, and thus extracting meaningful information.Pre-requisites: CS201 (DS) / CS506 (DSA) If already completed CS503 (ML), the reasonably good students (Grade A- or A in CS503) are recommended to take the Advanced Data Mining course (CS724) being offered in parallel.Just use it as an input in your program to read from, and perform the tasks.To know and understand the contents of the xml file, see the Dataset in PDF (dblp50000.pdf).The values of y and their corresponding values of y are shown in the table below, identify the linear regression model in the form y=mx b and report the values of m(slope) and b(intercept) as well as the estimated value of y when the value of x is 10What R command could we use to generate a scatterplot diagram of our data to determine if it forms a linear pattern that would be suitable for linear regression or a non-linear pattern that would require some other technique?The values of y and their corresponding values of y are shown in the table below, identify the linear regression model in the form y=mx b and report the values of m (slope) and b (intercept) as well as the estimated value of y when the value of x is 3. The income of a company that produces disaster equipment has been expressed as a linear regression model based upon the input variable which is the number of hurricanes projected for the upcoming hurricane season.