Statistics MS app / Worried about deficiencies

[kowtown]

I dropped out of a Tier 1 school 6+ years ago. My GPA started out stellar at a school with a reputation for grade deflation, but my non-major grades dropped over time until I finally stopped going to all my classes and received quite a few F's, killing my average. (From what I've read in forums like these, this need not be a death knell.) So I have a fair number of good Computer Science grades from there (halfway between a minor and a major) with the rest of it mostly irrelevant to Statistics, save a C in Linear Algebra, which is becoming a cause for some consternation!

I completely turned around and with a renewed interest in formal education enrolled at the local Tier 4 as a math major. I've got a 4.0 through a full year and intend to keep it, probably for exactly 60 cumulative hours. (I also expect to do well on the GREs.) From this institution I've got most of what's usually recommended:

[*] Calc III (multivariate)

[*] Advanced Calc I (sequences, functions, continuity, differentiation, Riemann integrals)

[*] Intro to Probability and Statistics (not very broad or deep, but with double integration)

[*] Probability Theory (first bit of Wackerly/Mendenhall/Scheaffer)

[*] Statistical Methods and Models I (applied linear models plus a SAS lab component)

Additionally I'll be taking the following in the spring, after I've already applied. I wish I could've taken them sooner but they're only offered once a year and the prereqs precluded me from taking them last year. I'll probably mention them in my SoP.

[*] Mathematical Statistics (the next bit of W/M/S)

[*] Stochastic Processes

I'll be applying this fall to graduate Statistics programs, and I'm undecided on what to take that semester. Tell me if it seems silly, but I'm worried about a perceived deficiency in Linear Algebra. I'm perfectly capable of brushing up on my own, but since I took the class many years ago and got a C, I'm a little worried about what the adcoms will think, since it's such an important base for everything else. Here are some options I have, the first two to improve that deficiency, the second two for some additional stats.

MATH 2xx - Linear Algebra and Applications

Matrix algebra and solutions of systems of linear equations, matrix inversion, determinants. Vector spaces, linear dependence, basis and dimension, subspaces. Inner products, Gram-Schmidt process. Linear transformations, matrices of a linear transformation. Eigenvalues and eigenvectors. Applications. Constructing and writing mathematical proofs. A transition between beginning calculus courses and upper-level mathematics courses.

PRQ: (Multivariate Calculus)

MATH 4xx - Numerical Linear Algebra

Roundoff errors and computer arithmetic. Direct and iterative methods for solving linear systems; norms and condition numbers, iterative refinement. Linear least squares problems: the normal equations and QR approach for overdetermined systems. Numerical methods for eigenvalues: an introduction to the QR iteration. Extensive use of computers.

PRQ: (Multivariate Calculus, Linear Algebra, and Computer Programming)

STAT 4xx - Statistical Methods and Models II

Continuation of STAT 4xx. Topics include factorial experiments: interactions, nested models, and randomized block designs. Categorical response data analysis: ordinal data, measures of association, Cochran-Mantel-Haenszel Test, logistic regression, and measures of agreement.

PRQ: (Statistical Methods and Models I)

STAT 4xx - Statistical Methods of Forecasting

Introduction to forecasting including use of regression in forecasting; removal and estimation of trend and seasonality; exponential smoothing; stochastic time series models; stochastic difference equations; autoregressive, moving average, and mixed models; model identification and estimation; diagnostic checking; and the use of time series models in forecasting.

PRQ: (Statistical Methods and Models I)

I've already missed the boat on a more advanced linear algebra (LA) course. I'd rather not take a 200-level introduction to proof course my senior year, especially after taking Advanced Calc. At the same time, this is a proof-based LA course that strong applicants will have As in. Numerical Linear Algebra (NLA), OTOH, is somewhat interesting to me, reviews essential LA topics at the beginning of the course, and teaches Matlab. (Re: skibum's recommendations: The text, at least, covers algorithms for SVD, QR, and Cholesky--see "Preparation for Statistics MA?" below.) NLA has applications in statistical computing. It's not what I plan to study at a higher level, but with my CS background, who knows. It may also remediate my absence from the computing world. Numerical Analysis is an option for the spring, but I think that would be too late for an ancillary course to have an impact.

Another stats course would be icing on the applied cake, but could help me secure another recommendation. LoRs were, unfortunately, an afterthought, but I should have two lined up soon. I don't really have a third professor that knows me very well as of right now, so one letter might have to come from a class I take this fall. Forecasting seems really interesting, but this is something I would like to take when I get where I'm going, and unfortunately it conflicts with Advanced Calc II (series and multivariate functions), which I'd prefer to have on my transcript this semester. Let me know if you disagree. The second methods and models course is less interesting, but could help me get a handle on what I want to study. Econometrics is also an option, though the course offered in the fall overlaps a great deal with the probability and regression I've already studied. The professor--who, though from the econ department, might give me a strong recommendation--does teach matrix theory in the course, but it's not in the course description. (Do adcoms even read descriptions or do they depend purely on title and what might be said in the SoP?) A more advanced class is taught in the spring, but, again, no one will care about the spring.

I suppose I should also state that I have an interest in economics and initially intended the Statistics MS to be a stepping stone to the Economics PhD, but I am far from committed to the latter and now see the former as an end in its own right. That said, if I don't take one of these stats courses I could fit another econ course that could help me decide. My main objective is to get into a good Statistics program though. I plan on working advanced math and econ courses into my master's program as long as the PhD is a possibility.

I ran the NLA course by someone in the stats department, and he seemed to think it was a decent idea, especially the Matlab part, but he didn't know much about placing students. The math advisor recommended Abstract Algebra I instead, since it's more rigorous, but it has almost no relevance beyond additional proof writing, which I think is fairly well covered by the more relevant (and more challenging, at least here) analysis courses.

I realize this has become quite a long post and I appreciative you taking the time to read all the way through it. I hope it is of at least some interest to you, perhaps as an indication of what other people are taking or thinking or fretting about. Any comments would be greatly appreciated, even if they are about my current mental state.

For recent posts on general preparation for the Statistics MA/MS, see Preparation for Statistics MA? and Would anyone accept me?

P.S. I'm not sure where I'm applying yet, but I'm leaning towards east coast cities as far as location, ideally NYC (a region I used to reside in). If anyone could give me an idea of what level I'm competitive at, that'd be greatly appreciated too.